Madsen, Per Printz
active participation in the future smart grid environment. One of the main obstacles for making optimal energy consumption is to have good predictions of the future energy consumption. This study is based on real consumption data from eight houses in Denmark. There are designed two different prediction...... models. It is shown that both of the predictions model produce a better consumption prediction then a naïve model. Seen in this perspective is it concluded that it is possible to use Artificial Neural Networks for predicting the energy consumption in ordinary family houses....
Wei, Sun; Lujin, Zhang; Jinhai, Zou; Siyi, Miao
In this paper, the adaptive control based on neural network is studied. Firstly, a neural network based adaptive robust tracking control design is proposed for robotic systems under the existence of uncertainties. In this proposed control strategy, the NN is used to identify the modeling uncertainties, and then the disadvantageous effects caused by neural network approximating error and external disturbances in robotic system are counteracted by robust controller. Especially the proposed cont...
Yu Wenwu [Department of Mathematics, Southeast University, Nanjing 210096 (China); Cao Jinde [Department of Mathematics, Southeast University, Nanjing 210096 (China)]. E-mail: firstname.lastname@example.org
In this Letter, a novel approach of encryption based on chaotic Hopfield neural networks with time varying delay is proposed. We use the chaotic neural network to generate binary sequences which will be used for masking plaintext. The plaintext is masked by switching of chaotic neural network maps and permutation of generated binary sequences. Simulation results were given to show the feasibility and effectiveness in the proposed scheme of this Letter. As a result, chaotic cryptography becomes more practical in the secure transmission of large multi-media files over public data communication network.
In this Letter, a novel approach of encryption based on chaotic Hopfield neural networks with time varying delay is proposed. We use the chaotic neural network to generate binary sequences which will be used for masking plaintext. The plaintext is masked by switching of chaotic neural network maps and permutation of generated binary sequences. Simulation results were given to show the feasibility and effectiveness in the proposed scheme of this Letter. As a result, chaotic cryptography becomes more practical in the secure transmission of large multi-media files over public data communication network
Tian Zhuangzhuang; Zhan Ronghui; Hu Jiemin; Zhang Jun
This study presents a new method of Synthetic Aperture Radar (SAR) image target recognition based on a convolutional neural network. First, we introduce a class separability measure into the cost function to improve this network’s ability to distinguish between categories. Then, we extract SAR image features using the improved convolutional neural network and classify these features using a support vector machine. Experimental results using moving and stationary target acquisition and recogni...
Full Text Available This paper proposes a novel neural-network-based adaptive hybrid-reflectance three-dimensional (3-D surface reconstruction model. The neural network combines the diffuse and specular components into a hybrid model. The proposed model considers the characteristics of each point and the variant albedo to prevent the reconstructed surface from being distorted. The neural network inputs are the pixel values of the two-dimensional images to be reconstructed. The normal vectors of the surface can then be obtained from the output of the neural network after supervised learning, where the illuminant direction does not have to be known in advance. Finally, the obtained normal vectors can be applied to integration method when reconstructing 3-D objects. Facial images were used for training in the proposed approach
Full Text Available This study presents a new method of Synthetic Aperture Radar (SAR image target recognition based on a convolutional neural network. First, we introduce a class separability measure into the cost function to improve this network’s ability to distinguish between categories. Then, we extract SAR image features using the improved convolutional neural network and classify these features using a support vector machine. Experimental results using moving and stationary target acquisition and recognition SAR datasets prove the validity of this method.
Grolinger, Katarina; Jerbic, Bojan; Vranjes, Bozo
The purpose of autonomous robot is to solve various tasks while adapting its behavior to the variable environment, expecting it is able to navigate much like a human would, including handling uncertain and unexpected obstacles. To achieve this the robot has to be able to find solution to unknown situations, to learn experienced knowledge, that means action procedure together with corresponding knowledge on the work space structure, and to recognize working environment. The planning of the intelligent robot behavior presented in this paper implements the reinforcement learning based on strategic and random attempts for finding solution and neural network approach for memorizing and recognizing work space structure (structural assignment problem). Some of the well known neural networks based on unsupervised learning are considered with regard to the structural assignment problem. The adaptive fuzzy shadowed neural network is developed. It has the additional shadowed hidden layer, specific learning rule and initialization phase. The developed neural network combines advantages of networks based on the Adaptive Resonance Theory and using shadowed hidden layer provides ability to recognize lightly translated or rotated obstacles in any direction.
Yan Zhu; Guanghua Zhang; Jing Qiu
The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and part...
Full Text Available The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and particle swarm optimization algorithm is proposed to optimize the weight and threshold value of BP neural network. After network traffic prediction experiment, we can conclude that optimized BP network traffic prediction based on PSO-ABC has high prediction accuracy and has stable prediction performance.
Chen, Zixuan; Wang, Xuewen; Xu, Zekai; Hou, Wenguang
DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.
A direct feedback control system based on fuzzy-recurrent neural network is proposed, and a method of training weights of fuzzy-recurrent neural network was designed by applying modified contract mapping genetic algorithm. Computer simul ation results indicate that fuzzy-recurrent neural network controller has perfect dynamic and static performances .
For the redundant manipulators, neural network is used to tackle the velocity inverse kinematics of robot manipulators. The neural networks utilized are multi-layered perceptions with a back-propagation training algorithm. The weight table is used to save the weights solving the inverse kinematics based on the different optimization performance criteria. Simulations verify the effectiveness of using neural network.
FU Qiang; HU Shang-xu; ZHAO Sheng-ying
An effective ensemble should consist of a set of networks that are both accurate and diverse. We propose a novel clustering-based selective algorithm for constructing neural network ensemble, where clustering technology is used to classify trained networks according to similarity and optimally select the most accurate individual network from each cluster to make up the ensemble. Empirical studies on regression of four typical datasets showed that this approach yields significantly smaller en semble achieving better performance than other traditional ones such as Bagging and Boosting. The bias variance decomposition of the predictive error shows that the success of the proposed approach may lie in its properly tuning the bias/variance trade-offto reduce the prediction error (the sum of bias2 and variance).
CHEN Ai-bin; CAI Zi-xing; HU De-wen
An on-demand distributed clustering algorithm based on neural network was proposed. The system parameters and the combined weight for each node were computed, and cluster-heads were chosen using the weighted clustering algorithm, then a training set was created and a neural network was trained. In this algorithm, several system parameters were taken into account, such as the ideal node-degree, the transmission power, the mobility and the battery power of the nodes. The algorithm can be used directly to test whether a node is a cluster-head or not. Moreover, the clusters recreation can be speeded up.
GAO Wei-xin; LUO Xian-jue
This paper presents a new algorithm based on Hopfield neural network to find the optimal solution for an electric distribution network. This algorithm transforms the distribution power network-planning problem into a directed graph-planning problem. The Hopfield neural network is designed to decide the in-degree of each node and is in combined application with an energy function. The new algorithm doesn't need to code city streets and normalize data, so the program is easier to be realized. A case study applying the method to a district of 29 street proved that an optimal solution for the planning of such a power system could be obtained by only 26 iterations. The energy function and algorithm developed in this work have the following advantages over many existing algorithms for electric distribution network planning: fast convergence and unnecessary to code all possible lines.
Matthew K Luka
Full Text Available Speech recognition is a key element of diverse applications in communication systems, medical transcription systems, security systems etc. However, there has been very little research in the domain of speech processing for African languages, thus, the need to extend the frontier of research in order to port in, the diverse applications based on speech recognition. Hausa language is an important indigenous lingua franca in west and central Africa, spoken as a first or second language by about fifty million people. Speech recognition of Hausa Language is presented in this paper. A pattern recognition neural network was used for developing the system.
Sher, Gene I
Though machine learning has been applied to the foreign exchange market for quiet some time now, and neural networks have been shown to yield good results, in modern approaches neural network systems are optimized through the traditional methods, and their input signals are vectors containing prices and other indicator elements. The aim of this paper is twofold, the presentation and testing of the application of topology and weight evolving artificial neural network (TWEANN) systems to automated currency trading, and the use of chart images as input to a geometrical regularity aware indirectly encoded neural network systems. This paper presents the benchmark results of neural network based automated currency trading systems evolved using TWEANNs, and compares the generalization capabilities of these direct encoded neural networks which use the standard price vector inputs, and the indirect (substrate) encoded neural networks which use chart images as input. The TWEANN algorithm used to evolve these currency t...
Jiřina Jr., Marcel; Jiřina, Marcel
Roč. 10, č. 3 (2000), s. 417-428. ISSN 1210-0552. [Neural Network World 2000. Prague, 09.07.2000-12.07.2000] Grant ostatní: MŠMT ČR(CZ) VS96047; MPO(CZ) RP-4210 Institutional research plan: AV0Z1030915 Keywords : neural network * classifier * hyperspheres * big -dimensional data Subject RIV: BA - General Mathematics
洪炳镕; 金飞虎; 郭琦
Hopfield neural network is a single layer feedforward neural network. Hopfield network requires some control parameters to be carefully selected, else the network is apt to converge to local minimum. An ant system is a nature inspired meta heuristic algorithm. It has been applied to several combinatorial optimization problems such as Traveling Salesman Problem, Scheduling Problems, etc. This paper will show an ant system may be used in tuning the network control parameters by a group of cooperated ants. The major advantage of this network is to adjust the network parameters automatically, avoiding a blind search for the set of control parameters.This network was tested on two TSP problems, 5 cities and 10 cities. The results have shown an obvious improvement.
Stadler, Domen; Kosel, Franc; Čelič, Damjan; Lipej, Andrej
In the article a new mesh deformation algorithm based on artificial neural networks is introduced. This method is a point-to-point method, meaning that it does not use connectivity information for calculation of the mesh deformation. Two already known point-to-point methods, based on interpolation techniques, are also presented. In contrast to the two known interpolation methods, the new method does not require a summation over all boundary nodes for one displacement calculation. The consequence of this fact is a shorter computational time of mesh deformation, which is proven by different deformation tests. The quality of the deformed meshes with all three deformation methods was also compared. Finally, the generated and the deformed three-dimensional meshes were used in the computational fluid dynamics numerical analysis of a Francis water turbine. A comparison of the analysis results was made to prove the applicability of the new method in every day computation.
Full Text Available One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.
In order to overcome the effect of the assumption between emissivity and wavelength on the measurement of true temperature and spectral emissivity for most engineering materials, a neural network based method is proposed for data processing while a blackbody furnace and three optical filters with known spectral transmittance curves were used to make up a true target. The experimental results show that the calculated temperatures are in good agreement with the temperature of the blackbody furnace, and the calculated spectral emissivity curves are in good agreement with the spectral transmittance curves of the filters. The method proposed has been proved to be an effective method for solving the problem of true temperature and emissivity measurement, and it can overcome the effect of the assumption between emissivity and wavelength on the measurement of true temperature and spectral emissivity for most engineering materials.
ZHANG Jin-long; YANG Lan-rong
Contractor Prequalification involves the screening of contractors by a project owner, according to a given set of criteria, in order to determine their competence to perform the work if awarded the construction contract. This paper introduces the capabilities of neural networks in solving problems related to contractor prequalification. The neural network systems for contractor prequalification has an input vector of 8 components and an output vector of 1 component. The output vector represents whether a contractor is qualified or not qualified to submit a bid on a project.
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole;
-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi...
WANG Zhenfei; ZHAI Guangqun; WANG Nengchao
An effective blind digital watermarking algorithm based on neural networks in the wavelet domain is presented. Firstly, the host image is decomposed through wavelet transform. The significant coefficients of wavelet are selected according to the human visual system (HVS) characteristics. Watermark bits are added to them. And then effectively cooperates neural networks to learn the characteristics of the embedded watermark related to them. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results and comparisons with other techniques prove the effectiveness of the new algorithm.
information . Each one of these cells acts as a simple processor. When individual cells interact with one another, the complex abilities of the brain are made possible. In neural networks, the input or data are processed by a propagation function that adds up the values of all the incoming data. The ending value is then compared with a threshold or specific value. The resulting value must exceed the activation function value in order to become output. The activation function is a mathematical function that a neuron uses to produce an output referring to its input value.  Figure 1 depicts this process. Neural networks usually have three components an input, a hidden, and an output. These layers create the end result of the neural network. A real world example is a child associating the word dog with a picture. The child says dog and simultaneously looks a picture of a dog. The input is the spoken word ''dog'', the hidden is the brain processing, and the output will be the category of the word dog based on the picture. This illustration describes how a neural network functions
Full Text Available Mobile ad-hoc network has challenge of the limited power to prolong the lifetime of the network, because power is a valuable resource in mobile ad-hoc network. The status of power consumption should be continuously monitored after network deployment. In this paper, we propose coverage aware neural network based power control routing with the objective of maximizing the network lifetime. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage. The simulation results show that the proposed scheme can be used in wide area of applications in mobile ad-hoc network.
Jiao, Limin; Liu, Yaolin
This paper proposed a systemic resolution scheme of Digital Elevation model (DEM) interpolation based on Artificial Neural Networks (ANNs). In this paper, we employ BP network to fit terrain surface, and then detect and eliminate the samples with gross errors. This paper uses Self-organizing Feature Map (SOFM) to cluster elevation samples. The study area is divided into many more homogenous tiles after clustering. BP model is employed to interpolate DEM in each cluster. Because error samples are eliminated and clusters are built, interpolation result is better. The case study indicates that ANN interpolation scheme is feasible. It also shows that ANN can get a more accurate result by comparing ANN with polynomial and spline interpolation. ANN interpolation doesn't need to determine the interpolation function beforehand, so manmade influence is lessened. The ANN interpolation is more automatic and intelligent. At the end of the paper, we propose the idea of constructing ANN surface model. This model can be used in multi-scale DEM visualization, and DEM generalization, etc.
费翔; 何小燕; 罗军舟; 吴介一; 顾冠群
Congestion control is one of the key problems in high-speed networks, such as ATM. In this paper, a kind of traffic prediction and preventive congestion control scheme is proposed using neural network approach. Traditional predictor using BP neural network has suffered from long convergence time and dissatisfying error. Fuzzy neural network developed in this paper can solve these problems satisfactorily. Simulations show the comparison among no-feedback control scheme,reactive control scheme and neural network based control scheme.
. Due to the composition of coal, particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.
Ge Guangying; Chen Lili; Xu Jianjian
Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively.
ZHAN Li; LUO Xi-shuang; ZHANG Tian-qiao
In order to make the static state feedback nonlinear decoupling control law for a kind of missile to be easy for implementation in practice, an improvement is discussed. The improvement method is to introduce a BP neural network to approximate the decoupling control laws which are designed for different aerodynamic characteristic points, so a new decoupling control law based on BP neural network is produced after the network training. The simulation results on an example illustrate the approach obtained feasible and effective.
Full Text Available To describe gene regulatory networks, this article takes the framework of the artificial genome model and proposes impulsive neural networks algorithm based on the artificial genome model. Firstly, the gene expression and the cell division tree are applied to generate spiking neurons with specific attributes, neural network structure, connection weights and specific learning rules of each neuron. Next, the gene segment duplications and divergence model are applied to design the evolutionary algorithm of impulsive neural networks at the level of the artificial genome. The dynamic changes of developmental gene regulatory networks are controlled during the whole evolutionary process. Finally, the behavior of collecting food for autonomous intelligent agent is simulated, which is driven by nerves. Experimental results demonstrate that the algorithm in this article has the evolutionary ability on large-scale impulsive neural networks
Miguel Angelo de Abreu de Sousa
Full Text Available Interconnections between electronic circuits and neural computation have been a strongly researched topic in the machine learning field in order to approach several practical requirements, including decreasing training and operation times in high performance applications and reducing cost, size, and energy consumption for autonomous or embedded developments. Field programmable gate array (FPGA hardware shows some inherent features typically associated with neural networks, such as, parallel processing, modular executions, and dynamic adaptation, and works on different types of FPGA-based neural networks were presented in recent years. This paper aims to address different aspects of architectural characteristics analysis on a Hopfield Neural Network implemented in FPGA, such as maximum operating frequency and chip-area occupancy according to the network capacity. Also, the FPGA implementation methodology, which does not employ multipliers in the architecture developed for the Hopfield neural model, is presented, in detail.
Gao Fei; Zhang Xiaohui
A method for predicting colored noise by introducing prediction of nonhnear time series is presented By adopting three kinds of neural networks prediction models, the colored noise prediction is studied through changing the filter bandwidth for stochastic noise and the sampling rate for colored noise The results show that colored noise can be predicted The prediction error decreases with the increasing of the sampling rate or the narrowing of the filter bandwidth. If the parameters are selected properly, the prediction precision can meet the requirement of engineering implementation. The results offer a new reference way for increasing the ability for detecting weak signal in signal processing system
Fan Zhang(张帆); Hongbin Zhang(张鸿宾)
In watermarking schemes, watermarking can be viewed as a form of communication problems. Almost all of previous works on image watermarking capacity are based on information theory, using Shannon formula to calculate the capacity of watermarking. In this paper, we present a blind watermarking algorithm using Hopfield neural network, and analyze watermarking capacity based on neural network. In our watermarking algorithm, watermarking capacity is decided by attraction basin of associative memory.
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole; Poulsen, Niels Kjølstad
-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....
Aizenberg, Igor; Paliy, Dmitriy V; Zurada, Jacek M; Astola, Jaakko T
A multilayer neural network based on multivalued neurons (MLMVN) is a neural network with a traditional feedforward architecture. At the same time, this network has a number of specific different features. Its backpropagation learning algorithm is derivative-free. The functionality of MLMVN is superior to that of the traditional feedforward neural networks and of a variety kernel-based networks. Its higher flexibility and faster adaptation to the target mapping enables to model complex problems using simpler networks. In this paper, the MLMVN is used to identify both type and parameters of the point spread function, whose precise identification is of crucial importance for the image deblurring. The simulation results show the high efficiency of the proposed approach. It is confirmed that the MLMVN is a powerful tool for solving classification problems, especially multiclass ones. PMID:18467216
Ma, Mingbo; Huang, Liang; Xiang, Bing; Zhou, Bowen
In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies. To exploit both deep learning and linguistic structures, we propose a tree-based convolutional neural network model which exploit various long-distance relationships between words. Our model improves the sequential baselines on all three sentiment and question classificat...
Boruah, Dibakor; Thakur, Pintu Kumar; Baruah, Dipal
The present study aims to quantify the applicability of artificial neural network as a black-box model for internal combustion engine performance. In consequence, an artificial neural network (ANN) based model for a four cylinder, four stroke internal combustion diesel engine has been developed on the basis of specific input and output factors, which have been taken from experimental readings for different load and engine speed circumstances. The input parameters that have been used to create...
Full Text Available With the development of computer science and technology, the development of artificial intelligence advances rapidly in the field of image restoration. Based on the MATLAB platform, this paper constructs a kind of image restoration technology of artificial intelligence based on the discrete neural network and feedforward network, and carries out simulation and contrast of the restoration process by the use of the bionic algorithm. Through the application of simulation restoration technology, this paper verifies that the discrete neural network has a good convergence and identification capability in the image restoration technology with a better effect than that of the feedforward network. The restoration technology based on the discrete neural network can provide a reliable mathematical model for this field.
This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method
This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness,on-line modelling and high precision.The maximum nonlinearity error can be reduced to 0.037% by using GNN.However, the maximum nonlinearity error is 0.075% using the least square method.
The mutual learning process between two parity feed-forward networks with discrete and continuous weights is studied analytically, and we find that the number of steps required to achieve full synchronization between the two networks in the case of discrete weights is finite. The synchronization process is shown to be non-self-averaging and the analytical solution is based on random auxiliary variables. The learning time of an attacker that is trying to imitate one of the networks is examined analytically and is found to be much longer than the synchronization time. Analytical results are found to be in agreement with simulations. (letter to the editor)
Wang Xing-Yuan; Zhang Yi
We propose a novel neural network based on a diagonal recurrent neural network and chaos,and its structure andlearning algorithm are designed.The multilayer feedforward neural network,diagonal recurrent neural network,and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map.The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks.
We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)
Full Text Available Engine fault has a high rate in the car. Considering about the distinguishing feature of the engine, Engine Diagnosis Expert System was investigated based on Diagnosis Tree module, Fuzzy Neural Network module, and commix reasoning module. It was researched including Knowledge base and Reasoning machine, and so on. In Diagnosis Tree module, the origin problem was searched in right method. In which module distinguishing rate and low error and least cost was the aim. By means of synthesize judge and fuzzy relation reasoning to get fault origin from symptom, fuzzy synthesize reasoning diagnosis module was researched. Expert knowledge included failure symptom, engine system failure and engine part failure. In the system, Self-diagnosis method and general instruments method worked together, complex failure diagnosis became efficient. The system was intelligent, which was combined by fuzzy logic reasoning and the traditional neural network system. And it became more convenience for failure origin searching, because of utilizing the three methods. The system fuzzy neural networks were combined with fuzzy reasoning and traditional neural networks. Fuzzy neural network failure diagnosis module of system, as a important model was applied to engine diagnosis, with more advantages such as higher efficiency of searching and higher self-learning ability, which was compared with the traditional BP network
刘涵; 刘丁; 李琦
A new nonlinear image filter using fuzzy neural network based on genetic algorithm is proposed. The learning of network parameters is performed by genetic algorithm with the efficient binary encoding scheme. In the following,fuzzy reasoning embedded in the network aims at restoring noisy pixels without degrading the quality of fine details. It is shown by experiments that the filter is very effective in removing impulse noise and significantly outperforms conventional filters.
A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network.
Ali Mohamed Yousef
The present study investigates the power system stabilizer based on neural predictive control for improving power system dynamic performance over a wide range of operating conditions. In this study a design and application of the Neural Network Model Predictive Controller (NN-MPC) on a simple power system composed of a synchronous generator connected to an infinite bus through a transmission line is proposed. The synchronous machine is represented in detail, taking into account the effect of ...
Yan Qiang; Bo Pei; Juanjuan Zhao
To avoid the drawbacks caused by the long-distance and large-area features of the outdoor forest fires in the traditional fire detection methods. A new forest fire recognition method based on the neural network is proposed, which recognizes the fire based on the static and dynamic features of the fire. The method combines the multiple parameters of the flames and the shapes of the fire to distinguish fire image. Then the extracted features were tested by the Back Propagation Neural Network. T...
We present an electron identification algorithm based on a neural network approach applied to the ZEUS uranium calorimeter. The study is motivated by the need to select deep inelastic, neutral current, electron proton interactions characterized by the presence of a scattered electron in the final state. The performance of the algorithm is compared to an electron identification method based on a classical probabilistic approach. By means of a principle component analysis the improvement in the performance is traced back to the number of variables used in the neural network approach. (orig.)
Hayakawa, Takashi; Aoyagi, Toshio
Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.
Full Text Available In order to make recognition for the composite taste, the paper puts forward the research on the composite taste recognition method based on fuzzy neural network based on the wavelet transform. According to the wavelet transformation and the compression and extraction of the data of the taste signals that are collected by the sensor, we use the fuzzy neutral network as the recognition tool of the taste signal. Besides, we add genetic algorithm to make the function optimization for the network weights and the data processing and fuzzy recognition of the composite taste signal are presented. Finally, we make the test for the network performance. The results show that it has feasibility and effectiveness that the fuzzy neural network is introduced into the fuzzy identification of the taste signals.
REN Xiaolin; HU Guangrui
In this paper,speech recognitionusing neural networks is investigated.Especially,chaotic dynamics is introduced to neurons,and a mul-tilayer chaotic neural network (MLCNN) architectureis built.A learning algorithm is also derived to trainthe weights of the network.We apply the MLCNNto speech recognition and compare the performanceof the network with those of recurrent neural net-work (RNN) and time-delay neural network (TDNN).Experimental results show that the MLCNN methodoutperforms the other neural networks methods withrespect to average recognition rate.
Zhao Qigang; Li Qunzhan; He Zhengyou
By using netflow traffic collecting technology, some traffic data for analysis are collected from a next generation network (NGN) operator. To build a wavelet basis neural network (NN), the Sigmoid function is replaced with the wavelet in NN. Then the wavelet multiresolution analysis method is used to decompose the traffic signal, and the decomposed component sequences are employed to train the NN. By using the methods, an NGN traffic prediction model is built to predict one day's traffic. The experimental results show that the traffic prediction method of wavelet NN is more accurate than that without using wavelet in the NGN traffic forecasting.
With the development of computer science and technology, the development of artificial intelligence advances rapidly in the field of image restoration. Based on the MATLAB platform, this paper constructs a kind of image restoration technology of artificial intelligence based on the discrete neural network and feedforward network, and carries out simulation and contrast of the restoration process by the use of the bionic algorithm. Through the application of simulation restoration technology, ...
Full Text Available This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person’s left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.
Full Text Available A modeling based on the improved Elman neural network (IENN is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL model, Chebyshev neural network (CNN model, and basic Elman neural network (BENN model, the proposed model has better performance.
Jie, Shao; Li, Wang; WeiSong, Zhao; YaQin, Zhong; Malekian, Reza
A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance. PMID:25054172
Moopenn, A.; Lambe, John; Thakoor, A. P.
An electronic embodiment of a neural network based associative memory in the form of a binary connection matrix is described. The nature of false memory errors, their effect on the information storage capacity of binary connection matrix memories, and a novel technique to eliminate such errors with the help of asymmetrical extra connections are discussed. The stability of the matrix memory system incorporating a unique local inhibition scheme is analyzed in terms of local minimization of an energy function. The memory's stability, dynamic behavior, and recall capability are investigated using a 32-'neuron' electronic neural network memory with a 1024-programmable binary connection matrix.
Barone, F.; Rosa, R; Eleuteri, A.; Garufi, F.; Milano, L; Tagliaferri, R.
In this paper a Neural Network based approach is presented to identify the noise in the VIRGO context. VIRGO is an experiment to detect Gravitational Waves by means of a Laser Interferometer. Preliminary results appear to be very promising for data analysis of realistic Interferometer outputs.
Safarik, Jakub; Slachta, Jiri
The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.
Full Text Available Computer is used by every people either at their work or at home. Our aim is to make computers that can understand human language and can develop a user friendly human computer interfaces (HCI. Human gestures are perceived by vision. The research is for determining human gestures to create an HCI. Coding of these gestures into machine language demands a complex programming algorithm. In this project, We have first detected, recognized and pre-processing the hand gestures by using General Method of recognition. Then We have found the recognized image’s properties and using this, mouse movement, click and VLC Media player controlling are done. After that we have done all these functions thing using neural network technique and compared with General recognition method. From this we can conclude that neural network technique is better than General Method of recognition. In this, I have shown the results based on neural network technique and comparison between neural network method & general method.
Lin, J.; J., Ming; Crookes, D.
Face recognition with unknown, partial distortion and occlusion is a practical problem, and has a wide range of applications, including security and multimedia information retrieval. The authors present a new approach to face recognition subject to unknown, partial distortion and occlusion. The new approach is based on a probabilistic decision-based neural network, enhanced by a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched loca...
Shupeng Zhao; Miao Tian; Shifang Zhang; Jiuxi Li; Lijuan Du; Ye Wang
Engine fault has a high rate in the car. Considering about the distinguishing feature of the engine, Engine Diagnosis Expert System was investigated based on Diagnosis Tree module, Fuzzy Neural Network module, and commix reasoning module. It was researched including Knowledge base and Reasoning machine, and so on. In Diagnosis Tree module, the origin problem was searched in right method. In which module distinguishing rate and low error and least cost was the aim. By means of synthesize judge...
Shekhar F. Lilhare
Full Text Available Paddy drying process depends upon ambient conditions, paddy quality, temperature and mass of hot drying air. Existing techniques of paddy drying process are highly nonlinear. In this paper, a neural network based automated controller for paddy drying is designed. The designed controller manages the steam temperature and blower motor speed to achieve constant paddy drying time. A Layer recurrent neural network is adopted for the controller. Atmospheric conditions such as temperature and humidity along with the size of the paddy are used as input to the network. Experimental results show that the developed controller can be used to control the paddy drying process. Implementation of developed controller will help in controlling the drying time at almost constant value which will definitely improve the quality of rice.
Full Text Available Most information received by the human is acquired through vision. However, image has the largest data amount in three information forms. If the image is not compressed, high transmission rate for digital image transmission and tremendous capacity for digital image storage can hinder the development of digital image. For example, for a color image whose resolution rate is 1280×1024, each pixel needs 24B for storage, and the total data amount is about 3.75MB. If the earth satellite transmits the acquired image to the earth at 30 frames per second, the transmitting data size in 1 second is about 112.5MB. Under the condition of the existing communication capacity, if the image is not compressed, the real-time transmission of most multimedia information can’t be completed. High-speed transmission and storage of digital image has become the biggest obstacle of promoting digital image communication. So it is necessary to compress image. Data compression not only can rapidly transmit various information sources, improve the utilization rage of information channel and reduce transmitted power, but also can save energy and reduce storage capacity. More and more attentions of people have been paid to the application of artificial neural network to image compression, the reason for which is that artificial neural network has good fault tolerance, self-organization and adaptivity compared with traditional compression methods. So the predetermined data coding algorithm is not needed in the process of image compression. Neural network can independently complete the image coding and compression according to the characteristics of image. The paper combines corner detection technology with artificial neural network image compression, and designs a new neural network image compression encoding based on corner block with reasonable structure, high compression rate and rapid convergence rate
A beam pattern evaluation method using neural network has been developed to assist non-expert cyclotron operators. While an expert operator can easily tell beam accelerating conditions by the beam pattern measured by a scanned beam probe, it is not easy for non-expert operators to evaluate the pattern. The followings are the summarized procedure of the proposed method. First, the features of the beam patterns, which correspond to the view points of the experts, are extracted using Gabor expansion. A neural network algorithm is applied to calculate the Gabor expansion. Next, the number of the extracted features is reduced by averaging the features of high frequency ranges in five partial zones. The idea of this process is based on the fact that the operators do not pay attention to the details of the high frequency components of the patterns. Finally, the pattern evaluation process by the expert operators is learned by the back-propagation algorithm on a multi-layered feed forward neural network. Parallel processing architecture of the feature extraction network, and the learning capability of the non-linear clustering network are very useful for the evaluation model of beam patterns. (author)
Ali Mohamed Yousef
Full Text Available The present study investigates the power system stabilizer based on neural predictive control for improving power system dynamic performance over a wide range of operating conditions. In this study a design and application of the Neural Network Model Predictive Controller (NN-MPC on a simple power system composed of a synchronous generator connected to an infinite bus through a transmission line is proposed. The synchronous machine is represented in detail, taking into account the effect of the machine saliency and the damper winding. Neural network model predictive control combines reliable prediction of neural network model with excellent performance of model predictive control using nonlinear Levenberg-Marquardt optimization. This control system is used the rotor speed deviation as a feedback signal. Furthermore, the using performance system of the proposed controller is compared with the system performance using conventional one (PID controller through simulation studies. Digital simulation has been carried out in order to validate the effectiveness proposed NN-MPC power system stabilizer for achieving excellent performance. The results demonstrate that the effectiveness and superiority of the proposed controller in terms of fast response and small settling time.
In the frame of an image interpretation system for automatic cartography based on remote sensing image classification improved by a photo interpreter knowledge, we propose a system using neural networks to produce fuzzy production rules. These rules are intended to describe class vegetation context relatively to out image data (generally a G.I.S.) as a human expert could do. In the system, the expert only gives samples of concerned classes via a G.U.I. (Graphic User Interface) connected to a G.I.S. In a first stage, a Kohonen neural network is used to found clusters and membership functions, and then to compute a first set of fuzzy 'IF-THEN' rules with certainty factors. The human expert then updates these rules, and the given samples, according to his own experience. Once satisfying and discriminating classification rules are found, a second kind of neural network using back propagation is used to tune the final set of rules. At the same time, it produces neural nets able to give for each pixel and each class, the realisation degree of the favourable context relatively to the knowledge inferred by the samples.
The authors investigated the strategies for optimizing neural networks under the unified frame based on task, focused for constructive neural networks on two typical and practical schemes, which are adaptively constructive neural networks by growing hidden or layers of hidden nodes and by growing sub net. With the Layer Multinet Model proposed by the research group, the authors investigated task-based algorithms for constructive neural networks, their perspective, strength and weakness
In a complex control process, instrument calibration is periodically performed to maintain the instruments within the calibration range, which assures proper control and minimizes down time. Instruments are usually calibrated under out-of-service conditions using manual calibration methods, which may cause incorrect calibration or equipment damage. Continuous in-service calibration monitoring of sensors and instruments will reduce unnecessary instrument calibrations, give operators more confidence in instrument measurements, increase plant efficiency or product quality, and minimize the possibility of equipment damage during unnecessary manual calibrations. In this dissertation, an artificial neural network (ANN)-based instrument calibration verification system is designed to achieve the on-line monitoring and verification goal for scheduling maintenance. Since an ANN is a data-driven model, it can learn the relationships among signals without prior knowledge of the physical model or process, which is usually difficult to establish for the complex non-linear systems. Furthermore, the ANNs provide a noise-reduced estimate of the signal measurement. More importantly, since a neural network learns the relationships among signals, it can give an unfaulted estimate of a faulty signal based on information provided by other unfaulted signals; that is, provide a correct estimate of a faulty signal. This ANN-based instrument verification system is capable of detecting small degradations or drifts occurring in instrumentation, and preclude false control actions or system damage caused by instrument degradation. In this dissertation, an automated scheme of neural network construction is developed. Previously, the neural network structure design required extensive knowledge of neural networks. An automated design methodology was developed so that a network structure can be created without expert interaction. This validation system was designed to monitor process sensors plant
Innovatively, neural network function programming in the BPNN (BP neural network) tool boxes from MATLAB are applied, and data processing is done about CYJ-101 pressure sensor, and the problem of the sensor temperature compensation is solved. The paper has made the pressure sensors major sensors and temperature sensor assistant sensors, input the voltage signal from the two sensors into the established BP neural network model, and done the simulation under the NN Toolbox environment of MATLAB...
Stepčenko, A; Borisovs, A
This article presents an overview of artificial neural network (ANN) applications in forecasting and possible forecasting accuracy improvements. Artificial neural networks are computational models and universal approximators, which can be applied to the time series forecasting with a high accuracy. A great rise in research activities was observed in using artificial neural networks for forecasting. This paper examines multi-layer perceptrons (MLPs) – back-propagation neur...
Mr. Rajesh Deshmukh
Full Text Available Accurate models for electric power load forecasting are essential to the operation and planning of a power utility company. Load forecasting helps electric utility to make important decisions on trading of power, load switching, and infrastructure development. Load forecasts are extremely important for power utilizes ISOs, financial institutions, and other stakeholder of power sector. Short term load forecasting is a essential part of electric power system planning and operation forecasting made for unit commitment and security assessment, which have a direct impact on operational casts and system security. Conventional ANN based load forecasting method deal with 24 hour ahead load forecasting by using forecasted temp. This can lead to high forecasting errors in case of rapid temperature changes. This paper present a neural network based approach for short term load forecasting considering data for training, validation and testing of neural network.
Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad
In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...... well as for DD1 lter and the DD2 lter, as well as functions for Unscented Kalman lters and several versions of particle lters. The toolbox requires MATLAB version 7, but no additional toolboxes are required.......In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...
Full Text Available Digital image watermarking has been proposed as a method to enhance medical data security, confidentiality and integrity. Medical image watermarking requires extreme care when embedding additional data, given their importance to clinical diagnosis, treatment, and research. In this paper, a novel image watermarking approach based on the human visual system (HVS model and neural network technique is proposed. The watermark was inserted into the middle frequency coefficients of the cover image’s blocked DCT based transform domain. In order to make the watermark stronger and less susceptible to different types of attacks, it is essential to find the maximum amount of interested watermark before the watermark becomes visible. In this paper, neural networks are used to implement an automated system of creating maximum-strength watermarks. The experimental results show that such method can survive of common image processing operations and has good adaptability for automated watermark embedding.
In light of the nonlinear approaching capability of artificial neural networks ( ANN), the term structure of interest rates is predicted using The generalized regression neural network (GRNN) and back propagation (BP) neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek's model and Cox-Ingersoll-Ross (CIR) model. The comparison reveals that neural network models outperform Vasicek's model and CIR model,which are more precise and closer to the real market situation.
Li Yingshun; Li Jingjing; Han Junfeng
With the development of computer science and technology, and increasingly intelligent industrial production, the application of big data in industry also advances rapidly, and the development of artificial intelligence in the aspect of fault diagnosis is particularly prominent. On the basis of MATLAB platform, this paper constructs a fault diagnosis expert system of artificial intelligence machine based on the probabilistic neural network, and it also carries out a simulation of production pr...
BASSEL SOLAIMANE; ADNENE CHERIF; SAMEH OUESLATI,
Digital image watermarking has been proposed as a method to enhance medical data security, confidentiality and integrity. Medical image watermarking requires extreme care when embedding additional data, given their importance to clinical diagnosis, treatment, and research. In this paper, a novel image watermarking approach based on the human visual system (HVS) model and neural network technique is proposed. The watermark was inserted into the middle frequency coefficients of the cover image’...
Cui, Yiming; Liu, Ting; Chen, Zhipeng; Wang, Shijin; Hu, Guoping
Reading comprehension has embraced a booming in recent NLP research. Several institutes have released the Cloze-style reading comprehension data, and these have greatly accelerated the research of machine comprehension. In this work, we firstly present Chinese reading comprehension datasets, which consist of People Daily news dataset and Children's Fairy Tale (CFT) dataset. Also, we propose a consensus attention-based neural network architecture to tackle the Cloze-style reading comprehension...
The application of neural networks in the data mining is very wide. Although neural networks may have complex structure, long training time, and uneasily understandable representation of results, neural networks have high acceptance ability for noisy data and high accuracy and are preferable in data mining. In this paper the data mining based on neural networks is researched in detail, and the key technology and ways to achieve the data mining based on neural networks are also researched.
Draghici, S. [Wayne State Univ., Detroit, MI (United States). Vision and Neural Networks Lab.; Beiu, V. [Los Alamos National Lab., NM (United States)
In recent years, multilayer feedforward neural networks (NN) have been shown to be very effective tools in many different applications. A natural and essential step in continuing the diffusion of these tools in day by day use is their hardware implementation which is by far the most cost effective solution for large scale use. When the hardware implementation is contemplated, the issue of the size of the NN becomes crucial because the size is directly proportional with the cost of the implementation. In this light, any theoretical results which establish bounds on the size of a NN for a given problem is extremely important. In the same context, a particularly interesting case is that of the neural networks using limited integer weights. These networks are particularly suitable for hardware implementation because they need less space for storing the weights and the fixed point, limited precision arithmetic has much cheaper implementations in comparison with its floating point counterpart. This paper presents an entropy based analysis which completes, unifies and correlates results partially presented in [Beiu, 1996, 1997a] and [Draghici, 1997]. Tight bounds for real and integer weight neural networks are calculated.
Full Text Available With the development of computer science and technology, and increasingly intelligent industrial production, the application of big data in industry also advances rapidly, and the development of artificial intelligence in the aspect of fault diagnosis is particularly prominent. On the basis of MATLAB platform, this paper constructs a fault diagnosis expert system of artificial intelligence machine based on the probabilistic neural network, and it also carries out a simulation of production process by the use of bionic algorithm. This paper makes a diagnosis of transformer fault by the use of an expert system developed by this paper, and verifies that the probabilistic neural network has a good convergence, fault-tolerant ability and big data handling capability in the fault diagnosis. It is suitable for industrial production, which can provide a reliable mathematical model for the construction of fault diagnosis expert system in the industrial production.
LIU Ting; LU Zhi-mao; LANG Jun; LI Sheng
The input of a network is the key problem for Chinese word sense disambiguation utilizing the neural network. This paper presents an input model of the neural network that calculates the mutual information between contextual words and the ambiguous word by using statistical methodology and taking the contextual words of a certain number beside the ambiguous word according to ( - M, + N). The experiment adopts triple-layer BP Neural Network model and proves how the size of a training set and the value of M and N affect the performance of the Neural Network Model. The experimental objects are six pseudowords owning three word-senses constructed according to certain principles. The tested accuracy of our approach on a closed-corpus reaches 90. 31% ,and 89. 62% on an open-corpus. The experiment proves that the Neural Network Model has a good performance on Word Sense Disambiguation.
Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.
Rajendra Palange,; Nishikant Pachpute
This paper proposes the Rainfall Prediction System by using classification technique. The advanced and modified neural network called Data Core Based Fuzzy Min Max Neural Network (DCFMNN) is used for pattern classification. This classification method is applied to predict Rainfall. The neural network called fuzzy min max neural network (FMNN) that creates hyperboxes for classification and predication, has a problem of overlapping neurons that resoled in DCFMNN to give greater accu...
Rana, A. S.; Zalzala, A.M.S.
A neural ntwork is proposed for collision detection among multiple robotic arms sharing a common workspace. The structure of the neural network is a hybrid between Guassian Radial Basis Function (RBF) neural networks and Multi-layer perceptron back-propagation (BP) neural networks. This network is used to generate potential fields in the configuration space of the robotic arms. A path planning algorithm based on heuristics is presented. It is shown that this algorithm works better than the c...
Todd Vollmer; Ondrej Linda; Milos Manic
Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.
Artificial Neural Networks and Genetic Algorithms are two relatively young research areas that were subject to a steadily growing interest during the past years. The structure of a neural network is a significant contributing factor to its performance and the structure is generally heuristically chosen. The use of evolutionary algorithms as search techniques has allowed different properties of neural networks to be evolved. This paper focuses on the intersection on neural networks and evolutionary computation, namely on how evolutionary algorithms can be used to assist neural network design and training, as a novel approach. In this research, a new evolvable artificial neural network modelling approach is presented, which utilizes an optimization process based on the combination of genetic algorithms and artificial neural networks, and is applied in the design of a neural network, oriented to solve the neutron spectrometry and simultaneous dosimetry problems, using only the count rates measured with a Bonner spheres spectrometer system as entrance data. (author)
NI Zhi-wei; JIA Rui-yu
On the basis of data mining and neural network, this paper proposes a general framework of the neural network expert system and discusses the key techniques in this kind of system. We apply these ideas on agricultural expert system to find some unknown useful knowledge and get some satisfactory results.
Full Text Available Internet protocol television (IPTV, being an emerging Internet application, plays an important and indispensable role in our daily life. In order to maximize user experience and on the same time to minimize service cost, we must take into pay attention to how to reduce the storage and transport costs. A lot of previous work has been done before to do this. There is a challenging problem in this: how to predict the popularities of videos as accurate as possible. To solve the problem, this paper presents a Neural Network model for the popularity prediction of the programs in the IPTV system. And we use the actual historical logs to validate our method. The historical logs are divided to two parts, one is used to train the neural network by extract input/output vectors, and the other part is used to verify the model. The experimental results from our validation show the Neural Network based method can gain better accuracy than the comparative method.
Sugunnasil, Prompong; Somhom, Samerkae
We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction.
Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.
Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.
The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection
Safa S. Ibrahim
Full Text Available In this paper, constructive learning is used to train the neural networks. The results of neural networks are obtained but its result is not in comprehensible form or in a black box form. Our goal is to use an important and desirable model to identify sets of input variable which results in a desired output value. The nature of this model can help to find an optimal set of difficult input variables. Accuracy. Genetic algorithms are used as an interpretation of achieving neural network inversion. On the other hand the inversion of neural network enables to find one or more input patterns which satisfy a specific output. The input patterns obtained from the genetic algorithm can be used for building neural network system explanation facilities.
Vajda, Igor; Grim, Jiří
Oxford : Eolss Publishers-UNESCO, 2008 - (Parra-Luna, F.), s. 224-248 ISBN 978-1-84826-654-4. - (Encyclopedia of Life Support Systems. Volume III) R&D Projects: GA ČR GA102/07/1594 Institutional research plan: CEZ:AV0Z10750506 Keywords : neural networks * probabilistic approach Subject RIV: BD - Theory of Information http://library.utia.cas.cz/separaty/2008/SI/vajda-systems science and cybernetics .pdf
Full Text Available In this paper, we present analysis of different training types for nonlinear autoregressive neural network, used for simulation of magnetic levitation system. First, the model of this highly nonlinear system is described and after that the Nonlinear Auto Regressive eXogenous (NARX of neural network model is given. Also, numerical optimization techniques for improved network training are described. It is verified that NARX neural network can be successfully used to simulate real magnetic levitation system if suitable training procedure is chosen, and the best two training types, obtained from experimental results, are described in details.
Implementation is a key bottleneck for tapping the vast potential of neural networks. In this paper the authors examine experimentally and theoretically two devices based on III-V technology, which are critical in the implementation of the Hopfield model as well as other neural type networks for associative memories. The devices are based on Stark effect of excitonic transitions. P-1 (multiquantum wells)-n structures using GaAs/AlGaAs provide a controller-modulator device which has integrating-thresholding properties required of neurons. The p-i-n structures also provide programmable modulators which can serve as a synaptic mask. Using Monte Carlo techniques they examine an all-optical architecture to implement the Hopfield network. No external feedback-thresholding circuitry is required in this implementation due to special design of the controller-modulator device. Speed and stability issues of this architecture are also addressed. The computer simulation results provide valuable insight into how the controller-modulator device should be improved for better network implementation. It is also important to note that the basic technology now exists for such an implementation
El-madany, Hanaa T.; Fahmy, Faten H.; El-Rahman, Ninet M. A.; Dorrah, Hassen T.
Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This work presents the spacecraft orbit determination, dimensioning of the renewable power system, and mathematical modeling of spacecraft power system which are required for simulating the system. The complete system is simulated using MATLAB-SIMULINK. The NN controller outperform PID in the extreme range of non-linearity. Well trained neural controller can operate at different conditions of load current at different orbital periods without any tuning such in case of PID controller. So an artificial neural network (ANN) based model has been developed for the optimum operation of spacecraft power system. An ANN is trained using a back propagation with Levenberg-Marquardt algorithm. The best validation performance is obtained for mean square error is equal to 9.9962×10 -11 at epoch 637. The regression between the network output and the corresponding target is equal to 100% which means a high accuracy. NNC architecture gives satisfactory results with small number of neurons, hence better in terms of memory and time are required for NNC implementation. The results indicate that the proposed control unit using ANN can be successfully used for controlling the spacecraft power system in low earth orbit (LEO). Therefore, this technique is going to be a very useful tool for the interested designers in space field.
Full Text Available This study puts forward one kind optimization controlling solution method on complicated system. At first modeling using neural network then adopt the real data to structure the neural network model of pertinence, make the parameter to seek to the neural network model excellently by mixing GA finally, thus got intelligence to the complicated system to optimize and control. The method can identify network configuration and network training methods. By adopting the number coding and effectively reducing the network size and the network convergence time, increase the network training speed. The study provides this and optimizes relevant MATLAB procedure which controls the method, so long as adjust a little to the concrete problem, can believe this procedure well the optimization of the complicated system controls the problem in the reality of solving.
LI Jiao; WANG Li-guo; ZHANG Ye; GU Yan-feng
A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel. The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information. Then the sub-pixel scaled target could be predicted by the trained model. In order to improve the performance of BP network, BP learning algorithm with momentum was employed. The experiments were conducted both on synthetic images and on hyperspectral imagery (HSI). The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.
The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....
Wuneng Zhou; Xueqing Yang; Jun Yang; Jun Zhou
The problem of stochastic synchronization of neutral-type neural networks with multidelays based on M-matrix is researched. Firstly, we designed a control law of stochastic synchronization of the neural-type and multiple time-delays neural network. Secondly, by making use of Lyapunov functional and M-matrix method, we obtained a criterion under which the drive and response neutral-type multiple time-delays neural networks with stochastic disturbance and Markovian switc...
Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie
Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.
Bodruzzaman, M.; Essawy, M.A.
The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. The phase-space attractors were constructed from the one dimensional time series data, using the time-delay embedding method, for both normal and abnormal conditions. Several identifying parameters were also computed from these attractors such as the correlation dimension, the Kolmogorov entropy, and the Lyapunov exponents. These chaotic attractor parameters can be used to discriminate between the normal and abnormal operating conditions of the FBC system. It was found that, the abnormal data has higher correlation dimension, larger Kolmogorov entropy and larger positive Lyapunov exponents as compared to the normal data. Chaotic system control using neural network based techniques were also investigated and compared to conventional chaotic system control techniques. Both types of chaotic system control techniques were applied to some typical chaotic systems such as the logistic, the Henon, and the Lorenz systems. A prototype model for real-time implementation of these techniques has been suggested to control the FBC system. These models can be implemented for real-time control in a next phase of the project after obtaining further measurements from the experimental model. After testing the control algorithms developed for the FBC model, the next step is to implement them on hardware and link them to
X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen
An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.
LI Chunyan; LI Ke; ZHANG Longyun; JIN Shengzhen; ZU Jifeng
Star sensor is an avionics instrument used to provide the absolute 3-axis attitude of a spacecraft by utilizing star observations. The key function is to recognize the observed stars by comparing them with the reference catalogue. Autonomous star pattern recognition requires that similar patterns can be distinguished from each other with a small training set. Therefore, a new method based on neural network technology is proposed and a recognition system containing parallel backpropagation (BP) multi-subnets is designed. The simulation results show that the method performs much better than traditional algorithms and the proposed system can achieve both higher recognition accuracy and faster recognition speed.
WANG Rui; WANG Xu; YANG Dan; FU Rong
In Traditional Chinese Medicine (TCM), it is an important parameter of the clinic disease diagnosis to analysis the pulse frequency. This article accords to pulse eight major essentials to identify pulse type of the pulse frequency classification based on back-propagation neural networks (BPNN). The pulse frequency classification includes slow pulse, moderate pulse, rapid pulse etc. By feature parameter of the pulse frequency analysis research and establish to identify system of pulse frequency features. The pulse signal from detecting system extracts period, frequency etc feature parameter to compare with standard feature value of pulse type. The result shows that identify-rate attains 92.5% above.
Shao Jie; Wang Li; Zhao WeiSong; Zhong YaQin; Reza Malekian
A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power...
Full Text Available Intelligent transportation systems (ITS focus to generate and spread creative services related to different transport modes for traffic management and hence enables the passenger informed about the traffic and to use the transport networks in a better way. Intelligent Trip Modeling System (ITMS uses machine learning to forecast the traveling speed profile for a selected route based on the traffic information available at the trip starting time. The intelligent Parking Information Guidance System provides an eminent Neural Network based intelligence system which provides automatic allocate ion of parking's through the Global Information system across the path of the users travel. In this project using efficient lookup table searches and a Lagrange-multiplier bisection search, Computational Optimized Allocation Algorithm converges faster to the optimal solution than existing techniques. The purpose of this project is to simulate and implement a real parking environment that allocates vacant parking slots using Allocation algorithm.
Christensen, Kim Hardam; Sørensen, Torben
This paper presents results from an experimentally based research on Gas Metal Arc Welding (GMAW), controlled by the artificial neural network (ANN) technology. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a high degree of quality in...... the challenging field of butt joint welding with full penetration under stochastically changing boundary conditions, e.g. major gap width variations. GMAW experiments performed on mild-steel plates (3 mm of thickness), show that high quality welds with uniform back-bead geometry are achievable for gap...... width variations from 0.5 mm to 2.3 mm - scanned 10 mm in front of the electrode location. In this research, the mapping from joint geometry and reference weld quality to significant welding parameters has been based on a static multi-layer feed-forward network. The Levenberg-Marquardt algorithm, for...
The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.
Nway Nway Kyaw Win
Full Text Available Abstract Power quality disturbances PQDs result serious problems in the reliability safety and economy of power system network. In order to improve electric power quality events the detection and classification of PQDs must be made type of transient fault. Software analysis of wavelet transform with multiresolution analysis MRA algorithm and feed forward neural network probabilistic and multilayer feed forward neural network based methodology for automatic classification of eight types of PQ signals flicker harmonics sag swell impulse fluctuation notch and oscillatory will be presented. The wavelet family Db4 is chosen in this system to calculate the values of detailed energy distributions as input features for classification because it can perform well in detecting and localizing various types of PQ disturbances. This technique classifies the types of PQDs problem sevents.The classifiers classify and identify the disturbance type according to the energy distribution. The results show that the PNN can analyze different power disturbance types efficiently. Therefore it can be seen that PNN has better classification accuracy than MLFF.
In general, we describe three different methods to select an appropriate distribution form:bistogram, probability plots, and hypothesis test. The life distribution is recognized by a neural network method. The relationship among life distribution with life data is described through threshold and weight of neural networks. The method is convenient to use. An example is presented to validate this method, and the results are satisfactory.
Kalyani A. Bhawar*, Prof. Nitin K. Bhil
MRI (Magnetic resonance Imaging) brain neoplasm pictures Classification may be a troublesome tasks due to the variance and complexity of tumors. This paper presents two Neural Network techniques for the classification of the magnetic resonance human brain images. The proposed Neural Network technique consists of 3 stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the options connected with tomography pictures victimization d...
Jian Wang; Zhenmin Tang; Xianli Jin
This study uses regression model and artificial neural network model to apply food safety index in food safety trend predication and makes policy advices in the construction and release of an authoritative food safety index, The results showed that the BP neural network was high-precision, fast and objective, which could be used to food safety evaluation of circulation links of production, processing and sales.
Full Text Available This study uses regression model and artificial neural network model to apply food safety index in food safety trend predication and makes policy advices in the construction and release of an authoritative food safety index, The results showed that the BP neural network was high-precision, fast and objective, which could be used to food safety evaluation of circulation links of production, processing and sales.
Full Text Available In a content-based image retrieval system (CBIR, the main issue is to extract the image features that effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of retrieval performance of image features. This paper presents a review of fundamental aspects of content based image retrieval including feature extraction of color and texture features. Commonly used color features including color moments, color histogram and color correlogram and Gabor texture are compared. The paper reviews the increase in efficiency of image retrieval when the color and texture features are combined. The similarity measures based on which matches are made and images are retrieved are also discussed. For effective indexing and fast searching of images based on visual features, neural network based pattern learning can be used to achieve effective classification.
Full Text Available In a content-based image retrieval system (CBIR, the main issue is to extract the image features that effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of retrieval performance of image features. This paper presents a review of fundamental aspects of content based image retrieval including feature extraction of color and texture features. Commonly used color features including color moments, color histogram and color correlogram and Gabor texture are compared. The paper reviews the increase in efficiency of image retrieval when the color and texture features are combined. The similarity measures based on which matches are made and images are retrieved are also discussed. For effective indexing and fast searching of images based on visual features, neural network based pattern learning can be used to achieve effective classification.
Mohammad Taghi Ameli
Full Text Available Transmission Network Expansion Planning (TNEP is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI tools such as Genetic Algorithm (GA, Simulated Annealing (SA, Tabu Search (TS and Artificial Neural Networks (ANNs are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs and Harmony Search Algorithm (HSA was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.
The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...
The United States real estate market is currently facing its worst hit in two decades due to the slowdown of housing sales. The most affected by this decline are real estate investors and home develop-ers who are currently struggling to break-even financially on their investments. For these investors, it is of utmost importance to evaluate the current status of the market and predict its performance over the short-term in order to make appropriate financial decisions. This paper presents the development of artificial neu-ral network based models to support real estate investors and home developers in this critical task. The pa-per describes the decision variables, design methodology, and the implementation of these models. The models utilize historical market performance data sets to train the artificial neural networks in order to pre-dict unforeseen future performances. An application example is analyzed to demonstrate the model capabili-ties in analyzing and predicting the market performance. The model testing and validation showed that the error in prediction is in the range between -2% and +2%.
Full Text Available Nowadays, number of vehicles has been increased and traditional systems of traffic controlling couldn’t be able to meet the needs that cause to emergence of Intelligent Traffic Controlling Systems. They improve controlling and urban management and increase confidence index in roads and highways. The goal of thisarticle is vehicles classification base on neural networks. In this research, it has been used a immovable camera which is located in nearly close height of the road surface to detect and classify the vehicles. The algorithm that used is included two general phases; at first, we are obtaining mobile vehicles in the traffic situations by using some techniques included image processing and remove background of the images and performing edge detection and morphology operations. In the second phase, vehicles near the camera areselected and the specific features are processed and extracted. These features apply to the neural networks as a vector so the outputs determine type of vehicle. This presented model is able to classify the vehicles in three classes; heavy vehicles, light vehicles and motorcycles. Results demonstrate accuracy of the algorithm and its highly functional level.
Highlights: ► Generalized regression neural network is used to predict the solar radiation on tilted surfaces. ► The above network, amongst many such as multilayer perceptron, is the most successful one. ► The present neural network returns a relative mean absolute error value of 9.1%. ► The present model leads to a mean absolute error value of estimate of 14.9 Wh/m2. - Abstract: The receiving ends of the solar energy conversion systems that generate heat or electricity from radiation is usually tilted at an optimum angle to increase the solar incident on the surface. Solar irradiation data measured on horizontal surfaces is readily available for many locations where such solar energy conversion systems are installed. Various equations have been developed to convert solar irradiation data measured on horizontal surface to that on tilted one. These equations constitute the conventional approach. In this article, an alternative approach, generalized regression type of neural network, is used to predict the solar irradiation on tilted surfaces, using the minimum number of variables involved in the physical process, namely the global solar irradiation on horizontal surface, declination and hour angles. Artificial neural networks have been successfully used in recent years for optimization, prediction and modeling in energy systems as alternative to conventional modeling approaches. To show the merit of the presently developed neural network, the solar irradiation data predicted from the novel model was compared to that from the conventional approach (isotropic and anisotropic models), with strict reference to the irradiation data measured in the same location. The present neural network model was found to provide closer solar irradiation values to the measured than the conventional approach, with a mean absolute error value of 14.9 Wh/m2. The other statistical values of coefficient of determination and relative mean absolute error also indicate the advantage of
FU Hua; SUN Shao-guang; XU Zhen-Iiang
In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limitations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advantages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With optimization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.
Full Text Available The networking infrastructure of wireless mesh networks (WMNs is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs. This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission. The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.
Kojić, Nenad; Reljin, Irini; Reljin, Branimir
The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic-i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance. PMID:22969360
Daud, T.; Moopenn, A.; Lamb, J. L.; Ramesham, R.; Thakoor, A. P.
A novel thin film approach to neural-network-based high-density associative memory is described. The information is stored locally in a memory matrix of passive, nonvolatile, binary connection elements with a potential to achieve a storage density of 10 to the 9th bits/sq cm. Microswitches based on memory switching in thin film hydrogenated amorphous silicon, and alternatively in manganese oxide, have been used as programmable read-only memory elements. Low-energy switching has been ascertained in both these materials. Fabrication and testing of memory matrix is described. High-speed associative recall approaching 10 to the 7th bits/sec and high storage capacity in such a connection matrix memory system is also described.
A novel concept of neural network based control in pulse-width modulation(PWM)voltage source inverters is presented.On the one hand,the optimal switching an-gles are obtained in real time by the neural network based controller；on the other hand,the output voltage is ad-justed to fit the expected value by neural network when input voltage or loads change.The structure of neural network is simple and easy to be realized by DSP hard-ware system.No large memory used for the existing opti-mal PWM schemes is required in the system.Theoreticalanlysis of the proposed so-called sparse neural network is provided,and the stability of the system is proved.Un-der the control of neural network the error of output volt-age descends sharply,and the system outputs ac voltage with high precision.
Reza Azmi; Mahdieh Hakimi; Zahra Bahmani
Multi-agent systems like Peer-to-Peer (P2P) Networks employ scalable mechanisms that allow anyone to offer content and services to other system users. The open accessibility of these networks makes them vulnerable to malicious users wishing to poison the system. This paper proposed a novel trust and reputation system, using RBF artificial neural network to determine trust level and mitigate the number of unreliable downloads.
Full Text Available This paperresearches on the prediction of traffic flow chaotic time series based on VNNTF neural network. First, the traffic flow time series chaotic feature is extracted by chaos theory. Pretreatment for traffic flow time series and the VNNTP neural networks model was build by this. Second, principles of neural network learning algorithm VNNTF is described. Based on chaotic learning algorithm, the neural network traffic Volterra learning algorithm isdesigned for fast learning algorithm. Last, a single-step prediction of traffic flow chaotic time series is researched by VNNTF network model based on chaotic algorithm. The results showed that the VNNTF network model predictive performance is better than the Volterra prediction filter and the BP neural network by the simulation results and root-mean-square value.
Full Text Available Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation (LMBP neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.
Aiming at the randomness and fuzziness of information security risk assessment factors of Internet of Things, cloud neural network information security risk assessment model was proposed, based on combination of cloud model and neural network and dynamic fusion of heterogeneous security factors. Focus on the research of normal cloud neural network evaluation method and judgment of global and multivalued dependencies characteristics between safety evaluation indicators and risk levels...
S. Kumar Chandar
Full Text Available The foreign currency exchange market is the highest and most liquid of the financial markets, with an estimated $1 trillion traded every day. Foreign exchange rates are the most important economic indices in the international financial markets. The prediction of them poses many theoretical and experimental challenges. This paper reports empirical proof that a neural network model is applicable to the prediction of foreign exchange rates. The exchange rates between Indian Rupee and four other major currencies, Pound Sterling, US Dollar, Euro and Japanese Yen are forecast by the trained neural networks. The neural network was trained by three different learning algorithms using historical data to find the suitable algorithm for prediction. The forecasting performance of the proposed system is evaluated using three statistical metrics and compared. The results presented here demonstrate that significantly close prediction can be made without extensive knowledge of market data.
Xu, Li; Tao, Gu
A neural network algorithm is proposed which can conceal different files effectively such as *.exe, *.com, *.doc, *.txt and self-defined file formats. First, the important contents of the file are coded into a binary array. The total number of 0s and 1s is N. Second, to make the neural network learn the sample space, N pixel values and their closely relevant pixel values are randomly chosen from a color BMP format image and used to train the neural network, thus we get a group of network parameters and its outputs Y1. Each element of Y1 is increased by 0 or 1 according to the zeros and ones from the array, the increased Y1is called Y2. Third, using the transmitted parameters, a receiver can restore the neural network. Network outputs Y3(Y1) can also be obtained by simulating the restored neural network with the seed pixel values. Finally, the encrypted information can be decoded by Y2 and Y3. The acquisition of parameters and Y1 is different when the neural network is trained each time, so the algorithm has the characteristic of a one-time pad, which can enhance the correspondence security. Because the network colligates the chosen pixel values and their closely relevant pixel values, a cryptanalyst can not restore the network parameters and Y3 easily. Without the seed picture and the password, he does not know the encrypted data even if he knows the network parameters and Y2. If he only has the seed picture, he does not know the encrypted contents either, because there is no other information in the picture, which just is used to train the network. Using the same algorithm, the fragile watermark can be embedded into Y1 simultaneously. Besides telling you whether Y2 or network parameters have been tampered with, the fragile watermark could as well, reflecting the distortion status in the spatial domain of the tampered image. Therefore, the proposed method is of significance in practice.
Gaurav Kumar Tak
Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal someconfidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used forphishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mailscan be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spammingis growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb themind-peace, waste time and consume various resources e.g., memory space and network bandwidth, sofiltering of spam mails is a big issue in cyber security.This paper presents an novel approach of spam filtering which is based on some query generatedapproach on the knowledge base and also use some artificial neural network methods to detect the spammails based on their behavior. analysis of the mail header, cross validation. Proposed methodologyincludes the 7 several steps which are well defined and achieve the higher accuracy. It works well with allkinds of spam mails (text based spam as well as image spam. Our tested data and experiments resultsshows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.
Gaurav Kumar Tak
Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal some confidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used for phishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mails can be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spamming is growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb the mind-peace, waste time and consume various resources e.g., memory space and network bandwidth, so filtering of spam mails is a big issue in cyber security. This paper presents an novel approach of spam filtering which is based on some query generated approach on the knowledge base and also use some artificial neural network methods to detect the spam mails based on their behavior. analysis of the mail header, cross validation. Proposed methodology includes the 7 several steps which are well defined and achieve the higher accuracy. It works well with all kinds of spam mails (text based spam as well as image spam. Our tested data and experiments results shows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.
Full Text Available With software’s increasing scale and complexity, software failure is inevitable. To date, although many kinds of software fault localization methods have been proposed and have had respective achievements, they also have limitations. In particular, for fault localization techniques based on machine learning, the models available in literatures are all shallow architecture algorithms. Having shortcomings like the restricted ability to express complex functions under limited amount of sample data and restricted generalization ability for intricate problems, the faults cannot be analyzed accurately via those methods. To that end, we propose a fault localization method based on deep neural network (DNN. This approach is capable of achieving the complex function approximation and attaining distributed representation for input data by learning a deep nonlinear network structure. It also shows a strong capability of learning representation from a small sized training dataset. Our DNN-based model is trained utilizing the coverage data and the results of test cases as input and we further locate the faults by testing the trained model using the virtual test suite. This paper conducts experiments on the Siemens suite and Space program. The results demonstrate that our DNN-based fault localization technique outperforms other fault localization methods like BPNN, Tarantula, and so forth.
Rai, Man Mohan
An aerodynamic design procedure that uses neural networks to model the functional behavior of the objective function in design space has been developed. This method incorporates several improvements to an earlier method that employed a strategy called parameter-based partitioning of the design space in order to reduce the computational costs associated with design optimization. As with the earlier method, the current method uses a sequence of response surfaces to traverse the design space in search of the optimal solution. The new method yields significant reductions in computational costs by using composite response surfaces with better generalization capabilities and by exploiting synergies between the optimization method and the simulation codes used to generate the training data. These reductions in design optimization costs are demonstrated for a turbine airfoil design study where a generic shape is evolved into an optimal airfoil.
By establishing some suitable partitions of input and output spaces, a novel fuzzy neuralnetwork (FNN) which is called selection type FNN is developed. Such a system is a multilayerfeedforward neural network, which can be a universal approximator with maximum norm. Based ona family of fuzzy inference rules that are of real senses, a simple and useful inference type FNN isconstructed. As a result, the fusion of selection type FNN and inference type FNN results in a novelfilter-FNN filter. It is simple in structure. And also it is convenient to design the learning algorithmfor structural parameters. Further, FNN filter can efficiently suppress impulse noise superimposed onimage and preserve fine image structure, simultaneously. Some examples are simulated to confirmthe advantages of FNN filter over other filters, such as median filter and adaptive weighted fuzzymean (AWFM) filter and so on, in suppression of noises and preservation of image structure.
Li, Huaqing; Liao, Xiaofeng; Li, Chuandong; Huang, Hongyu; Li, Chaojie
This paper studies a technique employing both cellular neural networks (CNNs) and linear matrix inequality (LMI) for edge detection of noisy images. Our main work focuses on training templates of noise reduction and edge detection CNNs. Based on the Lyapunov stability theorem, we derive a criterion for global asymptotical stability of a unique equilibrium of the noise reduction CNN. Then we design an approach to train edge detection templates, and this approach can detect the edge precisely and efficiently, i.e., by only one iteration. Finally, we illustrate performance of the proposed methodology from the aspect of peak signal to noise ratio (PSNR) through computer simulations. Moreover, some comparisons are also given to prove that our method outperforms classical operators in gray image edge detection.
Full Text Available Winding is a key process in the manufacturing process of textile industry. The normal and effective operation of winding process plays a very important role on the textiles’ quality and economic effects. At present, a large proportion of bobbins which collected from winder still have yarn left over. The bobbin recycling is severely limited and quick running of winder is seriously restricted, the invention of the the automatic bobbin sorting machine has solved this problem. The ability to distinguish bobbin which has yarn left over from the rest and the classification accuracy of color are the two important performance indicators for bobbin sorting machine. According to the development and application of the color recognition technology and the artificial intelligence method, this study proposes a novel color recognition method that based on BP neural networks. The result shows that the accuracy of color recognition reaches 98%.
We propose a scheme for the realization of artificial neural networks based on superconducting quantum interference devices (SQUIDs). In order to demonstrate the operation of this scheme we designed and successfully tested a small network that implements an XOR gate and is trained by means of examples. The proposed scheme can be particularly convenient as support for superconducting applications such as detectors for astrophysics, high energy experiments, medicine imaging and so on. (paper)
YANG Bin; ZHU Zhong-ying
Traditional PCA is a linear method, but most engineering problems are nonlinear. Using the linear PCA in nonlinear problems may bring distorted and misleading results. Therefore, an approach of nonlinear principal component analysis (NLPCA) using radial basis function (RBF) neural network is developed in this paper. The orthogonal least squares (OLS) algorithm is used to train the RBF neural network. This method improves the training speed and prevents it from being trapped in local optimization. Results of two experiments show that this NLPCA method can effectively capture nonlinear correlation of nonlinear complex data, and improve the precision of the classification and the prediction.
Full Text Available Transport network is a key factor of economic, social and every other form of development in the region and the state itself. One of the main conditions for transport network development is the construction of new routes. Often, the construction of regional roads is dominant, since the design and construction in urban areas is quite limited. The process of analysis and planning the new roads is a complex process that depends on many factors (the physical characteristics of the terrain, the economic situation, political decisions, environmental impact, etc. and can take several months. These factors directly or indirectly affect the final solution, and in combination with project limitations and requirements, sometimes can be mutually opposed. In this paper, we present one software solution that aims to find Pareto optimal path for preliminary design of the new roadway. The proposed algorithm is based on many different factors (physical and social with the ability of their increase. This solution is implemented using Hopfield's neural network, as a kind of artificial intelligence, which has shown very good results for solving complex optimization problems.
Based on the analysis of the structure of ART-2 and parallel BP neural network, a hybrid artificial neural network is proposed aiming at the intercurrent faults diagnosis of nuclear power plants. Firstly the ART-2 net is used to identify the single fault, then the parallel BP net is used to distinguish intercurrent faults from new fault. The simulation shows that, the hybrid artificial neural network resolves the problem of single neural network in distinguishing intercurrent faults from new fault, and can diagnose the intercurrent fault and new fault efficiently. (authors)
温淑焕; 王洪瑞; 吴丽艳
Force control based on neural networks is presented. Under the framework of hybrid control, an RBF neural network is used to compensate for all the uncertainties from robot dynamics and unknown environment first. The technique will improve the adaptability to environment stiffness when the end-effector is in contact with the environment, and does not require any a priori knowledge on the upper bound of syste uncertainties. Moreover, it need not compute the inverse of inertia matrix. Learning algorithms for neural networks to minimize the force error directly are designed. Simulation results have shown a better force/position tracking when neural network is used.
Considering the complexity of vegetables price forecast,the prediction model of vegetables price was set up by applying the neural network based on genetic algorithm and using the characteristics of genetic algorithm and neural work.Taking mushrooms as an example,the parameters of the model are analyzed through experiment.In the end,the results of genetic algorithm and BP neural network are compared.The results show that the absolute error of prediction data is in the scale of 10%;in the scope that the absolute error in the prediction data is in the scope of 20% and 15%.The accuracy of genetic algorithm based on neutral network is higher than the BP neutral network model,especially the absolute error of prediction data is within the scope of 20%.The accuracy of genetic algorithm based on neural network is obviously better than BP neural network model,which represents the favorable generalization capability of the model.
Full Text Available Abstract— This paper describes the application for teleoperations of the SCARA robot via the internet. The SCARA robot is used by students of mehatronics at the University of Maribor as a remote educational tool. The developed software consists of two parts i.e. the continuous neural network sliding mode controller (CNNSMC and the graphical user interface (GUI. Application is based on two well-known commercially available software packages i.e. MATLAB/Simulink and LabVIEW. Matlab/Simulink and the DSP2 Library for Simulink are used for control algorithm development, simulation and executable code generation. While this code is executing on the DSP-2 Roby controller and through the analog and digital I/O lines drives the real process, LabVIEW virtual instrument (VI, running on the PC, is used as a user front end. LabVIEW VI provides the ability for on-line parameter tuning, signal monitoring, on-line analysis and via Remote Panels technology also teleoperation. The main advantage of a CNNSMC is the exploitation of its self-learning capability. When friction or an unexpected impediment occurs for example, the user of a remote application has no information about any changed robot dynamic and thus is unable to dispatch it manually. This is not a control problem anymore because, when a CNNSMC is used, any approximation of changed robot dynamic is estimated independently of the remote’s user. Index Terms—LabVIEW; Matlab/Simulink; Neural network control; remote educational tool; robotics
This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.
Hofemann, C.; Van Bussel, G.J.W.; Veldkamp, H.
If at least one reference wind turbine is available, which provides sufficient information about the wind turbine loads, the loads acting on the neighbouring wind turbines can be predicted via an artificial neural network (ANN). This research explores the possibilities to apply such a network not only within a wind park but on turbines located at different sites. Following the idea to develop a tool to forecast the particular loads of any wind turbine in the field without the need to install ...
This paper presents a study results of the Artificial Neural Network (ANN) architectures [Self-Organizing Map (SOM) and Multi-Layer Perceptron (MLP)] using single beam echosounding data. The single beam echosounder, operable at 12 kHz, has been used...
BAO Xiao-an; LUO Zhuo-lin; ZHANG Rui-lin
A processing method on the basis of the technology of computer visual and digital image was introduced. The improved LVQ (learning vector quantization) neural network algorithm applied in the process to identify the grade of apples was proved effective in experiment.
Full Text Available The goal of Component Based Software Engineering (CBSE is to deliver high quality, more reliable and more maintainable software systems in a shorter time and within limited budget by reusing and combining existing quality components. A high quality system can be achieved by using quality components, framework and integration process that plays a significant role. So, techniques and methods used for quality assurance and assessment of a component based system is different from those of the traditional software engineering methodology. In this paper, we are presenting a model for optimizing Chidamber and Kemerer (CK metric values of component-based software. A deep analysis of a series of CK metrics of the software components design patterns is done and metric values are drawn from them. By using unsupervised neural network- Self Organizing Map, we have proposed a model that provides an optimized model for Software Component engineering model based on reusability that depends on CK metric values. Average, standard deviated and optimized values for the CK metric are compared and evaluated to show the optimized reusability of component based model.
This paper presents the results of the second phase of a three-phase Severe Accident Management expert system program underway. The primary objectives of the second phase were to develop and demonstrate four capabilities of neural networks with respect to nuclear power plant severe accident monitoring and prediction. A second objective of the program was to develop an interactive graphical user interface which presented the system's information in an easily accessible and straightforward manner to the user. This paper describes the technical and regulatory foundation upon which the expert system is based and provides a background on the development of a new severe accident management tool. This tool provides data to assist in; (1) planning and developing priorities for recovery actions, (2) evaluating recovery action feasibility, (3) identifying recovery action options, and (4) assessing the timing and possible effects of potential recovery strategies. These performance characteristics represent the goals identified for the Severe Accident Management Strategies Online Network (SAMSON) which is currently under development. 4 refs, 1 fig., 1 tab
WU Bo; WU Ke; LU JianHong
Based on detailed atudy on aeveral kinds of fuzzy neural networks, we propose a novel compensation. based recurrent fuzzy neural network (CRFNN) by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure Identification of the CRFNN In order to confirm the fuzzy rules and their correlaUve parameters effectively. Furthermore, we Improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.
WU Xiao-guang; SHI Zhong-kun
The factors that influence underwater vehicle sonar self-noise are analyzed, and genetic algorithms and a back propagation (BP) neural network are combined to predict underwater vehicle sonar self-noise. The experimental results demonstrate that underwater vehicle sonar self-noise can be predicted accurately by a GA-BP neural network that is based on actual underwater vehicle sonar data.
Spicker, J.S.; Wikman, F.; Lu, M.L.;
We have trained an artificial neural network to predict the sequence of the human TP53 tumor suppressor gene based on a p53 GeneChip. The trained neural network uses as input the fluorescence intensities of DNA hybridized to oligonucleotides on the surface of the chip and makes between zero and...
This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.
Zhang Cuiping; Yang Qingfo
According to advantages of neural network and characteristics of operating procedures of engine, a new strategy is represented on the control of fuel injection and ignition timing of gasoline engine based on improved BP network algorithm. The optimum ignition advance angle and fuel injection pulse band of engine under different speed and load are tested for the samples training network, focusing on the study of the design method and procedure of BP neural network in engine injection and ignition control. The results show that artificial neural network technique can meet the requirement of engine injection and ignition control. The method is feasible for improving power performance, economy and emission performances of gasoline engine.
This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix
Mjalli, F.S.; Al-Asheh, S. [Chemical Engineering Department, Qatar University, Doha (Qatar)
In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves. (Abstract Copyright , Wiley Periodicals, Inc.)
Full Text Available Due to the larger workload in the implementation process and the poor consistence between the test results and actual situation problems when using the transmission line pulse (TLP testing methods, a modeling method based on the recurrent neural network is proposed for EMP response forecast. Based on the TLP testing system, two categories of EMP are increased, which are the machine model ESD EMP and human metal model ESD EMP. Elman neural network, Jordan neural network and their combination namely Elman-Jordan neural network are established for response modeling of NUP2105L transient voltage suppressor (TVS forecasting the response under different EMP. The simulation results show that the recurrent neural network has satisfying modeling effects and high computation efficiency.
Huaiqin Wu; Luying Zhang; Sanbo Ding; Xueqing Guo; Lingling Wang
This paper investigates the complete periodic synchronization of memristor-based neural networks with time-varying delays. Firstly, under the framework of Filippov solutions, by using M-matrix theory and the Mawhin-like coincidence theorem in set-valued analysis, the existence of the periodic solution for the network system is proved. Secondly, complete periodic synchronization is considered for memristor-based neural networks. According to the state-dependent switching feature of the memrist...
Christopher L. Pulliam, MS
Full Text Available Upper-limb amputation can cause a great deal of functional impairment for patients, particularly for those with amputation at or above the elbow. Our long-term objective is to improve functional outcomes for patients with amputation by integrating a fully implanted electromyographic (EMG recording system with a wireless telemetry system that communicates with the patient's prosthesis. We believe that this should generate a scheme that will allow patients to robustly control multiple degrees of freedom simultaneously. The goal of this study is to evaluate the feasibility of predicting dynamic arm movements (both flexion/extension and pronation/supination based on EMG signals from a set of muscles that would likely be intact in patients with transhumeral amputation. We recorded movement kinematics and EMG signals from seven muscles during a variety of movements with different complexities. Time-delayed artificial neural networks were then trained offline to predict the measured arm trajectories based on features extracted from the measured EMG signals. We evaluated the relative effectiveness of various muscle subsets. Predicted movement trajectories had average root-mean-square errors of approximately 15.7° and 24.9° and average R2 values of approximately 0.81 and 0.46 for elbow flexion/extension and forearm pronation/supination, respectively.
Mathiyalagan, K; Anbuvithya, R; Sakthivel, R; Park, Ju H; Prakash, P
In this paper, we formulate and investigate the mixed H∞ and passivity based synchronization criteria for memristor-based recurrent neural networks with time-varying delays. Some sufficient conditions are obtained to guarantee the synchronization of the considered neural network based on the master-slave concept, differential inclusions theory and Lyapunov-Krasovskii stability theory. Also, the memristive neural network is considered with two different types of memductance functions and two types of gain variations. The results for non-fragile observer-based synchronization are derived in terms of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed criterion is demonstrated through numerical examples. PMID:26655373
Based on radial basis function (RBF) neural networks, the healthy working model of each sub-system of robot in FMS is established. A new approach to fault on-line detection and diagnosis according to neural networks model is presented. Fault double detection based on neural network model and threshold judgement and quick fault identification based on multi-layer feedforward neural networks are applied, which can meet quickness and reliability of fault detection and diagnosis for robot in FMS.
Full Text Available In this paper, a new Simulink model for a neural network controlled PWM AC chopper fed single phase induction motor is proposed. Closed loop speed control is achieved using a neural network controller. To maintain a constant fluid flow with a variation in pressure head, drives like fan and pump are operated with closed loop speed control. The need to improve the quality and reliability of the drive circuit has increased because of the growing demand for improving the performance of motor drives. With the increased availability of MOSFET's and IGBT's, PWM converters can be used efficiently in low and medium power applications. From the simulation studies, it is seen that the PWM AC chopper has a better harmonic spectrum and lesser copper loss than the Phase controlled AC chopper. It is observed that the drive system with the proposed model produces better dynamic performance, reduced overshoot and fast transient response. .
De Lara, Alejandro Chinea Manrique
The assessment of highly-risky situations at road intersections have been recently revealed as an important research topic within the context of the automotive industry. In this paper we shall introduce a novel approach to compute risk functions by using a combination of a highly non-linear processing model in conjunction with a powerful information encoding procedure. Specifically, the elements of information either static or dynamic that appear in a road intersection scene are encoded by using directed positional acyclic labeled graphs. The risk assessment problem is then reformulated in terms of an inductive learning task carried out by a recursive neural network. Recursive neural networks are connectionist models capable of solving supervised and non-supervised learning problems represented by directed ordered acyclic graphs. The potential of this novel approach is demonstrated through well predefined scenarios. The major difference of our approach compared to others is expressed by the fact of learning t...
Xia, Youshen; Wang, Jun
This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. PMID:25913233
S. Kumar Chandar; Sumathi, Dr. M.; Dr S. N. Sivanandam
The foreign currency exchange market is the highest and most liquid of the financial markets, with an estimated $1 trillion traded every day. Foreign exchange rates are the most important economic indices in the international financial markets. The prediction of them poses many theoretical and experimental challenges. This paper reports empirical proof that a neural network model is applicable to the prediction of foreign exchange rates. The exchange rates between Indian Rupee and four other...
Chlada, Milan; Blaháček, Michal; Převorovský, Zdeněk
Brno : VUT Brno, 2005 - (Mazal, P.), s. 55-62 ISBN 80-214-2996-8. [NDT in Progress. Praha (CZ), 10.10.2005-12.10.2005] R&D Projects: GA ČR(CZ) GA201/04/2102; GA MPO FT-TA/026 Institutional research plan: CEZ:AV0Z20760514 Keywords : AE source location * neural network s * signal features Subject RIV: BI - Acoustics
Considering the difficulty of creating water quality model for activated sludge system, a typical BP artificial neural network model has been established to simulate the operation of a waste water treatment facilities. The comparison of prediction results with the on-spot measurements shows the model, the model is accurate and this model can also be used to realize intelligentized on-line control of the wastewater processing process.
Sher, Gene I.
Though machine learning has been applied to the foreign exchange market for algorithmic trading for quiet some time now, and neural networks(NN) have been shown to yield positive results, in most modern approaches the NN systems are optimized through traditional methods like the backpropagation algorithm for example, and their input signals are price lists, and lists composed of other technical indicator elements. The aim of this paper is twofold: the presentation and testing of the applicati...
Esmaelpoor, Jamal; Ghafouri, Abdollah
Network on chip (NoC) has emerged as a solution to overcome the system on chip growing complexity and design challenges. A proper routing algorithm is a key issue of an NoC design. An appropriate routing method balances load across the network channels and keeps path length as short as possible. This survey investigates the performance of a routing algorithm based on Hopfield Neural Network. It is a dynamic programming to provide optimal path and network monitoring in real time. The aim of this article is to analyse the possibility of using a neural network as a router. The algorithm takes into account the path with the lowest delay (cost) form source to destination. In other words, the path a message takes from source to destination depends on network traffic situation at the time and it is the fastest one. The simulation results show that the proposed approach improves average delay, throughput and network congestion efficiently. At the same time, the increase in power consumption is almost negligible.
Frolov, A. A.; Polyakov, P.Y.; Húsek, Dušan; Řezanková, H.
Heidelberg : Springer, 2006 - (Rizzi, A.; Vichi, M.), s. 861-868 ISBN 3-7908-1708-2. [COMPSTAT 2006. Symposium /17./. Rome (IN), 28.08.2006-01.09.2006] R&D Projects: GA AV ČR 1ET100300419; GA ČR GA201/05/0079 Grant ostatní: RFBR(RU) 05-07-90049 Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean factor analysis * neural networks * social networks Subject RIV: BB - Applied Statistics, Operational Research
Holographic neural networks are a new and promising type of artificial neural networks. This article gives an overview of the holographic neural technology and its possibilities. The theoretical principles of holographic networks are first reviewed. Then, some other papers are presented, where holographic networks have been applied or experimentally evaluated. A case study dealing with currency exchange rate prediction is described in more detail.
Full Text Available Bombardier Aerospace’s high performance aircrafts and services set the utmost standard for the Aerospace industry. A case study in collaboration with Bombardier Aerospace is conducted in order to estimate the target cost of a landing gear. More precisely, the study uses both parametric model and neural network models to estimate the cost of main landing gears, a major aircraft commodity. A comparative analysis between the parametric based model and those upon neural networks model will be considered in order to determine the most accurate method to predict the cost of a main landing gear. Several trials are presented for the design and use of the neural network model. The analysis for the case under study shows the flexibility in the design of the neural network model. Furthermore, the performance of the neural network model is deemed superior to the parametric models for this case study.
The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given . It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.
Full Text Available In order to speed up the defects of the neural network computing and recognition, the essay proposes the information identification method research of sports throwing force based on the fuzzy neural network model. Firstly, I use the information, which is the combination of the wavelet transformation and the fuzzy neural network, to identify the new method combining and make the noise-suppressed processing of information. Then, according to the athlete’s throwing action and the extraction of signal processing characteristics, as well as the analysis of the fuzzy neural network algorithm. Finally, in order to verify the effectiveness of the proposed algorithm, I make analysis for the experimental results, which indicates that using this algorithm can not only have less noise than the traditional algorithm, but also have less number of the neural network computation. Besides, its recognition speed and accuracy is also higher.
MARABA, V. A.
Full Text Available This paper deals with the structure and characteristics of PID Neural Network controller for single input and single output systems. PID Neural Network is a new kind of controller that includes the advantages of artificial neural networks and classic PID controller. Functioning of this controller is based on the update of controller parameters according to the value extracted from system output pursuant to the rules of back propagation algorithm used in artificial neural networks. Parameters obtained from the application of PID Neural Network training algorithm on the speed model of the asynchronous motor exhibiting second order linear behavior were used in the real time speed control of the motor. Programmable logic controller (PLC was used as real time controller. The real time control results show that reference speed successfully maintained under various load conditions.
Mhd Saeed Sharif
Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.
Andina, D.; Bermúdez-Valbuena, R.
Artificial Neural Networks (ANNs) are a mathematical model of the main known characteristics of biological brian dynamics. ANNs inspired in biological reality have been useful to design machines that show some human-like behaviours. Based on them, many experimentes have been succesfully developed emulating several biologial neurons characteristics, as learning how to solve a given problem. Sometimes, experimentes on ANNs feedback to biology and allow advances in understanding the biological brian behaviour, allowing the proposal of new therapies for medical problems involving neurons performing. Following this line, the author present results on artificial learning on ANN, and interpret them aiming to reinforce one of this two didactic estrategies to learn how to solve a given difficult task: a) To train with clear, simple, representative examples and feel confidence in brian generalization capabilities to achieve succes in more complicated cases. b) To teach with a set of difficult cases of the problem feeling confidence that the brian will efficiently solve the rest of cases if it is able to solve the difficult ones. Results may contribute in the discussion of how to orientate the design innovative succesful teaching strategies in the education field.
Grimaldi, Vincent; Politano, Jean-Luc
In accordance with its missions, le Centre de Recherches et d'Etudes de la Logistique de la Police Nationale francaise (CREL) has been conducting research for the past few years targeted at detecting drugs and explosives. We have focused our approach of the underlying physical and chemical detection principles on solid state gas sensors, in the hope of developing a hand-held drugs and explosives detector. The CREL and Laboratory and Scientific Services Directorate are research partners for this project. Using generic hydrocarbon, industrially available, metal oxide sensors as illicit material detectors, requires usage precautions. Indeed, neither the product's concentrations, nor even the products themselves, belong to the intended usage specifications. Therefore, the CREL is currently investigating two major research topics: controlling the sensor's environment: with environmental control we improve the detection of small product concentration; determining detection thresholds: both drugs and explosives disseminate low gas concentration. We are attempting to quantify the minimal concentration which triggers detection. In the long run, we foresee a computer-based tool likely to detect a target gas in a noisy atmosphere. A neural network is the suitable tool for interpreting the response of heterogeneous sensor matrix. This information processing structure, alongside with proper sensor environment control, will lessen the repercussions of common MOS sensor sensitivity characteristic dispersion.
Alfaro-Ponce, M; Argüelles, A; Chairez, I
This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artificial neural networks (NN) described by ordinary differential equations (ODEs). The training method for this kind of continuous NN (CNN) was developed according to the Lyapunov theory stability analysis. A parallel structure with fixed weights was proposed to perform the classification stage. The pattern recognition efficiency was validated by two methods, a generalization-regularization and a k-fold cross validation (k=5). The classifier was applied on two different databases. The first one was made up by signals collected from patients suffering of epilepsy and it is divided in five different classes. The second database was made up by 90 single EEG trials, divided in three classes. Each class corresponds to a different visual evoked potential. The pattern recognition algorithm achieved a maximum correct classification percentage of 97.2% using the information of the entire database. This value was similar to some results previously reported when this database was used for testing pattern classification. However, these results were obtained when only two classes were considered for the testing. The result reported in this study used the whole set of signals (five different classes). In comparison with similar pattern recognition methods that even considered less number of classes, the proposed CNN proved to achieve the same or even better correct classification results. PMID:27131469
Sparks, Thomas C; Crouse, Gary D; Dripps, James E; Anzeveno, Peter; Martynow, Jacek; Deamicis, Carl V; Gifford, James
Improvements in the efficacy and spectrum of the spinosyns, novel fermentation derived insecticide, has long been a goal within Dow AgroSciences. As large and complex fermentation products identifying specific modifications to the spinosyns likely to result in improved activity was a difficult process, since most modifications decreased the activity. A variety of approaches were investigated to identify new synthetic directions for the spinosyn chemistry including several explorations of the quantitative structure activity relationships (QSAR) of spinosyns, which initially were unsuccessful. However, application of artificial neural networks (ANN) to the spinosyn QSAR problem identified new directions for improved activity in the chemistry, which subsequent synthesis and testing confirmed. The ANN-based analogs coupled with other information on substitution effects resulting from spinosyn structure activity relationships lead to the discovery of spinetoram (XDE-175). Launched in late 2007, spinetoram provides both improved efficacy and an expanded spectrum while maintaining the exceptional environmental and toxicological profile already established for the spinosyn chemistry. PMID:18344004
The present paper deals with the application of an Extended Kalman filter based adaptive Neural-Network control scheme to improve the performance of a hot strip rolling mill. The suggested Neural Network model was implemented using Bayesian Evidence based training algorithm. The control input was estimated iteratively by an on-line extended Kalman filter updating scheme basing on the inversion of the learned neural networks model. The performance of the controller is evaluated using an accurate model estimated from real rolling mill input/output data, and the usefulness of the suggested method is proved
HAN Liu-xin; WANG Huan-chen; ZHANG Xian-hui
A detailed study of the capabilities of artificial neural networks to diagnoses cracks in massive concrete structures is presented. This paper includes the components of the expert system such as design thought, basic structure, building of knowledge base and the implementation of neural network applied model. The realizing method of neural network based clustering algorithm in the knowledge base and selfstudy is analyzed emphatically and stimulated by means of the computer. From the above study, some important conclusions have been drawn and some new viewpoints have been suggested.
Zhang, Wei; Li, Chuandong; Huang, Tingwen; He, Xing
Synchronization of an array of linearly coupled memristor-based recurrent neural networks with impulses and time-varying delays is investigated in this brief. Based on the Lyapunov function method, an extended Halanay differential inequality and a new delay impulsive differential inequality, some sufficient conditions are derived, which depend on impulsive and coupling delays to guarantee the exponential synchronization of the memristor-based recurrent neural networks. Impulses with and without delay and time-varying delay are considered for modeling the coupled neural networks simultaneously, which renders more practical significance of our current research. Finally, numerical simulations are given to verify the effectiveness of the theoretical results. PMID:26054076
A novel H∞ design methodology for a neural network-based nonlinear filtering scheme is addressed.Firstly,neural networks are employed to approximate the nonlinearities.Next,the nonlinear dynamic system is represented by the mode-dependent linear difference inclusion (LDI).Finally,based on the LDI model,a neural network-based nonlinear filter (NNBNF) is developed to minimize the upper bound of H∞ gain index of the estimation error under some linear matrix inequality (LMI) constraints.Compared with the existing nonlinear filters,NNBNF is time-invariant and numerically tractable.The validity and applicability of the proposed approach are successfully demonstrated in an illustrative example.
This paper first puts forward a case-based system framework basedon data mining techniques. Then the paper examines the possibility of using neural n etworks as a method of retrieval in such a case-based system. In this system we propose data mining algorithms to discover case knowledge and other algorithms.
Several methods for evaluating the sublayer suspension beneath old pavement with falling weight deflectormeter(FWD), were summarized and the respective advantages and disadvantages were analyzed. Based on these methods, the evaluation principles were improved and a new type of the neural network, functional-link neural network was proposed to evaluate the sublayer suspension with FWD test results. The concept of function link, learning method of functional-link neural network and the establishment process of neural network model were studied in detail. Based on the old pavement over-repairing engineering of Kaiping section, Guangdong Province in G325 National Highway, the application of functional-link neural network in evaluation of sublayer suspension beneath old pavement based on FWD test data on the spot was investigated. When learning rate is 0.1 and training cycles are 405, the functional-link network error is less than 0.0001, while the optimum chosen 4-8-1 BP needs over 10000 training cycles to reach the same accuracy with less precise evaluation results. Therefore, in contrast to common BP neural network,the functional-link neural network adopts single layer structure to learn and calculate, which simplifies the network, accelerates the convergence speed and improves the accuracy. Moreover the trained functional-link neural network can be adopted to directly evaluate the sublayer suspension based on FWD test data on the site. Engineering practice indicates that the functional-link neural model gains very excellent results and effectively guides the pavement over-repairing construction.
Full Text Available To seek optimal network parameters of Radial Basis Function (RBF Neural Network and improve the accuracy of this method on estimation of soil property space, this study utilizes genetic algorithm to optimize three network parameters of RBF Neural Network including the number of hidden layer nodes, expansion speed and root-mean-square error. Then, based on optimized RBF Neural Network, spatial interpolation is conducted for arable soil property under different sampling scales in the study area. The estimation result is superior to RBF Neural Network method without optimization and geostatistical method in terms of the fitting capacity and interpolation accuracy. Compared with the result of space estimation by RBF Neural Network method without optimization, among the 5 schemes, the forecast errors of RBF Neural Network optimized by genetic algorithm reduce greatly. Mean absolute error (MAE reduces 0.4868 on the average and root-mean-square error (RMSE reduces 1.492 on the average. Therefore, RBF Neural Network method optimized by genetic algorithm can gain the information about regional soil property spatial variation more accurately and provides technical support for arable land quality evaluation, accurate farmland management and rational application of fertilizer.
Full Text Available Paper presents a Neural Network Modelling approach to microwave LNA design. To acknowledge the specifications of the amplifier, Mobile Satellite Systems are analyzed. Scattering parameters of the LNA in the frequency range 0.5 to 18 GHz are calculated using a Multilayer Perceptron Artificial Neural Network model and corresponding smith charts and polar charts are plotted as output to the model. From these plots, the microwave scattering parameter description of the LNA are obtained. Model is efficiently trained using Agilent ATF 331M4 InGaAs/InP Low Noise pHEMT amplifier datasheet and the neural model’s output seem to follow the various device characteristic curves with high regression. Next, Maximum Allowable Gain and Noise figure of the device are modelled and plotted for the same frequency range. Finally, the optimized model is utilized as an interpolator and the resolution of the amplifying capability with noise characteristics are obtained for the L Band of MSS operation.
The motion planning of multiple mobile robots undertaking individual tasks in the same environment is studied. A motion planning method based on neural networks is proposed. By storing fuzzy rules in neural networks the method can fully make use of the association ability and high processing speed of neural networks to make robots avoid collisions with other objects in real time.Compared with rules method,the method can not only avoid building a large and complex rules base but also make robots avoid collisions and conflicts at higher speed and with higher intelligence.
Yuan Haiying; Chen Guangju; Xie Yongle
Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit.The feature evaluation and extraction methods based on neural network are presented.Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently.The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency.A fault diagnosis illustration validated this method.
Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.
To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.
Li, Ning; Cao, Jinde
This paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the ω-measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results. PMID:26462246
In recent years, several neural networks using Clifford algebra have been studied. Clifford algebra is also called geometric algebra. Complex-valued Hopfield neural networks (CHNNs) are the most popular neural networks using Clifford algebra. The aim of this brief is to construct hyperbolic HNNs (HHNNs) as an analog of CHNNs. Hyperbolic algebra is a Clifford algebra based on Lorentzian geometry. In this brief, a hyperbolic neuron is defined in a manner analogous to a phasor neuron, which is a typical complex-valued neuron model. HHNNs share common concepts with CHNNs, such as the angle and energy. However, HHNNs and CHNNs are different in several aspects. The states of hyperbolic neurons do not form a circle, and, therefore, the start and end states are not identical. In the quantized version, unlike complex-valued neurons, hyperbolic neurons have an infinite number of states. PMID:24808287
Peláez, J R
Pattern completion in a neural network model of the thalamus and a biologically plausible model of synaptic plasticity are the key concepts used in this paper for analyzing some cognitive disorders that involve hallucinations of several kinds: visual hallucinations in the Charles Bonnet syndrome and psychedelic drugs consumption, somatic hallucination in phantom limbs, cognitive hallucinations in schizophrenia and even in multiple personality disorders. All these types of hallucinations are proposed to be the result of a pattern completion dynamics performed in thalamic deafferented areas. Effective treatments of some of these disorders involve peripheral stimulation jointly with a central inhibition so that the neural circuits generating the disorders are depressed according to the proposed model of synaptic plasticity. PMID:11156194
Taghavi Kani M
Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.
Full Text Available To further improve the performance of feed-forward neural network blind equalization based on Constant Modulus Algorithm (CMA cost function, an instantaneous gradient based dual mode between Modified Constant Modulus Algorithm (MCMA and Decision Directed (DD algorithm was proposed. The neural network weights change quantity of the adjacent iterative process is defined as instantaneous gradient. After the network converges, the weights of neural network to achieve a stable energy state and the instantaneous gradient would be zero. Therefore dual mode algorithm can be realized by criterion which set according to the instantaneous gradient. Computer simulation results show that the dual mode feed-forward neural network blind equalization algorithm proposed in this study improves the convergence rate and convergence precision effectively, at the same time, has good restart and tracking ability under channel burst interference condition.
The aim of this paper is to develop a multiscale hierarchical hybrid model based on finite element analysis and neural network computation to link mesoscopic scale (trabecular network level) and macroscopic (whole bone level) to simulate bone remodelling process. Because whole bone simulation considering the 3D trabecular level is time consuming, the finite element calculation is performed at macroscopic level and a trained neural network are employed as numerical devices for substituting the finite element code needed for the mesoscale prediction. The bone mechanical properties are updated at macroscopic scale depending on the morphological organization at the mesoscopic computed by the trained neural network. The digital image-based modeling technique using m-CT and voxel finite element mesh is used to capture 2 mm3 Representative Volume Elements at mesoscale level in a femur head. The input data for the artificial neural network are a set of bone material parameters, boundary conditions and the applied str...
Based on the traditional optimization methods about the pressure control spring of the relief valve and combined with the advantages of neural network, this paper put forward the optimization method with many parameters and a lot of constraints based on neural network. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure control spring of the relief valve is set up in this method which also puts forward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure control spring and improves the performance target.
Based on an integrate-and-fire mechanism, we investigate the effect of changing the efficacy of the synapse, the transmitting time-delayed, and the relative refractoryperiod on the self-organized criticality in our neural network model.
ZHOULi-Ming; ZHANGYing-Yue; CHENTian-Lun
Based on an integrate-and-fire mechanism, we investigate the effect of changing the efficacy of the synapse,the transmitting time-delayed, and the relative refractoryperiod on the self-organized criticality in our neural network model.
Full Text Available a critical point is a point at which the derivatives of an error function are all zero. It has been shown in the literature that critical points caused by the hierarchical structure of a real-valued neural network (NN can be local minima or saddle points, although most critical points caused by the hierarchical structure are saddle points in the case of complex-valued neural networks. Several studies have demonstrated that singularity of those kinds has a negative effect on learning dynamics in neural networks. As described in this paper, the decomposition of high-dimensional neural networks into low-dimensional neural networks equivalent to the original neural networks yields neural networks that have no critical point based on the hierarchical structure. Concretely, the following three cases are shown: (a A 2-2-2 real-valued NN is constructed from a 1-1-1 complex-valued NN. (b A 4-4-4 real-valued NN is constructed from a 1-1-1 quaternionic NN. (c A 2-2-2 complex-valued NN is constructed from a 1-1-1 quaternionic NN. Those NNs described above do not suffer from a negative effect by singular points during learning comparatively because they have no critical point based on a hierarchical structure.
Xu Qinzhen; Yang Luxi; Zhao Qiangfu; He Zhenya
Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network,statistical techniques and expert systems are used to model network packets in the field of intrusion detection.In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is presented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN's capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually "gray boxes" as they can be interpreted easily ifthe number of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset.We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.
Wang Lunwen; Zhang Ling
In this letter, Constructive Neural Networks (CNN) is used in large-scale data mining. By introducing the principle and characteristics of CNN and pointing out its deficiencies, fuzzy theory is adopted to improve the covering algorithms. The threshold of covering algorithms is redefined. "Extended area" for test samples is built. The inference of the outlier is eliminated. Furthermore, "Sphere Neighborhood (SN)" are constructed. The membership functions of test samples are given and all of the test samples are determined accordingly. The method is used to mine large wireless monitor data (about 3 × 107 data points), and knowledge is found effectively.
Hasan, M. A.; Reaz, M. B. I.
The aim of this paper is to model the algorithm for Fetal ECG (FECG) extraction from composite abdominal ECG (AECG) using VHDL (Very High Speed Integrated Circuit Hardware Description Language) for FPGA (Field Programmable Gate Array) implementation. Artificial Neural Network that provides efficient and effective ways of separating FECG signal from composite AECG signal has been designed. The proposed method gives an accuracy of 93.7% for R-peak detection in FHR monitoring. The designed VHDL model is synthesized and fitted into Altera's Stratix II EP2S15F484C3 using the Quartus II version 8.0 Web Edition for FPGA implementation.
Full Text Available - Gestures are powerful tools for non-verbal communication. Human computer interface (HCI is a growing field which reduces the complexity of interaction between human and machine in which gestures are used for conveying information or controlling the machine. In the present paper, static hand gestures are utilized for this purpose. The paper presents a novel technique of recognizing hand gestures i.e. A-Z alphabets, 0-9 numbers and 6 additional control signals (for keyboard and mouse control by extracting various features of hand ,creating a feature vector table and training a neural network. The proposed work has a recognition rate of 99%. .
This paper proposes a method for assembly cost estimation in actual manufacture during the design phase using artificial neural networks (ANN). It can support the de signers in cost effectiveness, then help to control the total cost. The method was used in the assembly cost estimation of the crucial parts of some railway stock products. As a compari son, we use the linear regression (LR) model in the same field. The result shows that ANN model performs better than the LR model in assembly cost estimation.
Cui, Xueli; Yu, Yongguang; Wang, Hu; Hu, Wei
In this paper, the memristor-based fractional-order neural networks with time delay are analyzed. Based on the theories of set-value maps, differential inclusions and Filippov’s solution, some sufficient conditions for asymptotic stability of this neural network model are obtained when the external inputs are constants. Besides, uniform stability condition is derived when the external inputs are time-varying, and its attractive interval is estimated. Finally, numerical examples are given to verify our results.
Subhra Rani Patra; R. Jehadeesan; Rajeswari, S.
This work provides the construction of Genetic Algorithm based Neural Network for parameter estimation of Fast Breeder Test Reactor (FBTR) Subsystem. The parameter estimated here is temperature of Intermediate Heat Exchanger of Fast Breeder Test Reactor. Genetic Algorithm based Neural Network is a global search algorithm having less probability of being trapped in local minimum problem as compared to Standard Back Propagation algorithm which is a local search algorithm. The various developmen...
Dong, Hui; Ling, Rongyao; Zhang, Dan
This paper is concerned with the network-based H∞ synchronization control for a class of discrete time-delay neural networks, and attention is focused on how to reduce the communication rate since the communication resource is limited. Techniques such as the measurement size reduction, signal quantization and stochastic signal transmission are introduced to achieve the above goal. An uncertain switched system model is first proposed to capture the above-networked uncertainties. Based on the switched system theory and Lyapunov stability approach, a sufficient condition is obtained such that the closed-loop synchronization system is exponentially stable in the mean-square sense with a prescribed H∞ performance level. The controller gains are determined by solving a set of linear matrix inequalities (LMIs). A numerical example is finally presented to show the effectiveness of the proposed design method.
Anbuvithya, R; Mathiyalagan, K; Sakthivel, R; Prakash, P
This paper addresses the passivity problem for a class of memristor-based bidirectional associate memory (BAM) neural networks with uncertain time-varying delays. In particular, the proposed memristive BAM neural networks is formulated with two different types of memductance functions. By constructing proper Lyapunov-Krasovskii functional and using differential inclusions theory, a new set of sufficient condition is obtained in terms of linear matrix inequalities which guarantee the passivity criteria for the considered neural networks. Finally, two numerical examples are given to illustrate the effectiveness of the proposed theoretical results. PMID:27468321
Jiang, Minghui; Wang, Shuangtao; Mei, Jun; Shen, Yanjun
This paper presents a global and local finite-time synchronization control law for memristor neural networks. By utilizing the drive-response concept, differential inclusions theory, and Lyapunov functional method, we establish several sufficient conditions for finite-time synchronization between the master and corresponding slave memristor-based neural network with the designed controller. In comparison with the existing results, the proposed stability conditions are new, and the obtained results extend some previous works on conventional recurrent neural networks. Two numerical examples are provided to illustrate the effective of the design method. PMID:25536233
JI Guangrong; SUN Jie; ZHAO Wencang; ZHANG Hande
This paper proposes a red tide monitoring method based on clustering and modular neural networks. To obtain the features of red tide from a mass of aerial remote sensing hyperspectral data, first the Log Residual Correction (LRC) is used to normalize the data, and then clustering analysis is adopted to select and form the training samples for the neural networks. For rapid monitoring, the discriminator is composed of modular neural networks, whose structure and learning parameters are determined by an Adaptive Genetic Algorithm (AGA). The experiments showed that this method can monitor red tide rapidly and effectively.
田永青; 李志; 朱仲英
Tax is very important to the whole country, so a scientific tax predictive model is needed. This paper introduces the theory of the cloud model. On this basis, it presents a cloud neural network, and analyzes the main factors which influence the tax revenue. Then if proposes a tax predictive model based on the cloud neural network. The model combines the strongpoints of the cloud model and the neural network. The experiment and simulation results show the effectiveness of the algorithm in this paper.
This paper presents a new kind of back propagation neural network (BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the survey and analysis of RBPNN for the classification of remote sensing multi-spectral image is discussed.The successful application of RBPNN to a land cover classification illustrates the simple computation and high accuracy of the new neural network and the flexibility and practicality of this new approach.
Ravi Narayan; Singh, V.P.; Chakraverty, S.
This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze t...
The noise identification model of the neural networks is established for the 63SCY14-1B hydraulic axial piston pump. Taking four kinds of different port plates as instances, the noise identification is successfully carried out for hydraulic axial piston pump based on experiments with the MATLAB and the toolbox of neural networks. The operating pressure, the flow rate of hydraulic axial piston pump, the temperature of hydraulic oil, and bulk modulus of hydraulic oil are the main parameters having influences on the noise of hydraulic axial piston pump. These four parameters are used as inputs of neural networks, and experimental data of the noise are used as outputs of neural networks. Error of noise identification is less than 1% after the neural networks have been trained. The results show that the noise identification of hydraulic axial piston pump is feasible and reliable by using artificial neural networks. The method of noise identification with neural networks is also creative one of noise theoretical research for hydraulic axial piston pump.
Naghsh-Nilchi, Ahmad R.; Kadkhodamohammadi, A. Rahim
An electrocardiogram (ECG) beat classification scheme based on multiple signal classification (MUSIC) algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP) neural network and a probabilistic neural network (PNN), are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.
Full Text Available An electrocardiogram (ECG beat classification scheme based on multiple signal classification (MUSIC algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP neural network and a probabilistic neural network (PNN, are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.
Khan, Usman T.; Valeo, Caterina
A new fuzzy neural network method to predict minimum dissolved oxygen (DO) concentration in a highly urbanised riverine environment (in Calgary, Canada) is proposed. The method uses abiotic factors (non-living, physical and chemical attributes) as inputs to the model, since the physical mechanisms governing DO in the river are largely unknown. A new two-step method to construct fuzzy numbers using observations is proposed. Then an existing fuzzy neural network is modified to account for fuzzy number inputs and also uses possibility theory based intervals to train the network. Results demonstrate that the method is particularly well suited to predicting low DO events in the Bow River. Model performance is compared with a fuzzy neural network with crisp inputs, as well as with a traditional neural network. Model output and a defuzzification technique are used to estimate the risk of low DO so that water resource managers can implement strategies to prevent the occurrence of low DO.
LI Zhe-min; CUI Li-guo; XU Shi-wei; WENG Ling-yun; DONG Xiao-xia; LI Gan-qiong; YU Hai-peng
This paper establishes a short-term prediction model of weekly retail prices for eggs based on chaotic neural network with the weekly retail prices of eggs from January 2008 to December 2012 in China. In the process of determining the structure of the chaotic neural network, the number of input layer nodes of the network is calculated by reconstructing phase space and computing its saturated embedding dimension, and then the number of hidden layer nodes is estimated by trial and error. Finally, this model is applied to predict the retail prices of eggs and compared with ARIMA. The result shows that the chaotic neural network has better nonlinear iftting ability and higher precision in the prediction of weekly retail price of eggs. The empirical result also shows that the chaotic neural network can be widely used in the ifeld of short-term prediction of agricultural prices.
Boulanger, Jean-Philippe [LODYC, UMR CNRS/IRD/UPMC, Paris (France); University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Martinez, Fernando; Segura, Enrique C. [University of Buenos Aires, Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Penalba, Olga [University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina)
Daily weather generators are used in many applications and risk analyses. The present paper explores the potential of neural network architectures to design daily weather generator models. Focusing this first paper on precipitation, we design a collection of neural networks (multi-layer perceptrons in the present case), which are trained so as to approximate the empirical cumulative distribution (CDF) function for the occurrence of wet and dry spells and for the precipitation amounts. This approach contributes to correct some of the biases of the usual two-step weather generator models. As compared to a rainfall occurrence Markov model, NNGEN-P represents fairly well the mean and standard deviation of the number of wet days per month, and it significantly improves the simulation of the longest dry and wet periods. Then, we compared NNGEN-P to three parametric distribution functions usually applied to fit rainfall cumulative distribution functions (Gamma, Weibull and double-exponential). A data set of 19 Argentine stations was used. Also, data corresponding to stations in the United States, in Europe and in the Tropics were included to confirm the results. One of the advantages of NNGEN-P is that it is non-parametric. Unlike other parametric function, which adapt to certain types of climate regimes, NNGEN-P is fully adaptive to the observed cumulative distribution functions, which, on some occasions, may present complex shapes. On-going works will soon produce an extended version of NNGEN to temperature and radiation. (orig.)
Shen, Wei; Yang, Feng; Mu, Wei; Yang, Caiyun; Yang, Xin; Tian, Jie
Localization of the vertebrae is of importance in many medical applications. For example, the vertebrae can serve as the landmarks in image registration. They can also provide a reference coordinate system to facilitate the localization of other organs in the chest. In this paper, we propose a new vertebrae localization method using convolutional neural networks (CNN). The main advantage of the proposed method is the removal of hand-crafted features. We construct two training sets to train two CNNs that share the same architecture. One is used to distinguish the vertebrae from other tissues in the chest, and the other is aimed at detecting the centers of the vertebrae. The architecture contains two convolutional layers, both of which are followed by a max-pooling layer. Then the output feature vector from the maxpooling layer is fed into a multilayer perceptron (MLP) classifier which has one hidden layer. Experiments were performed on ten chest CT images. We used leave-one-out strategy to train and test the proposed method. Quantitative comparison between the predict centers and ground truth shows that our convolutional neural networks can achieve promising localization accuracy without hand-crafted features.
Castillo, Oscar; Kacprzyk, Janusz
This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...
Current design method for circular sliding slopes is not so reasonable that it often results in slope sliding. As a result, artificial neural network (ANN) is used to establish an artificial neural network based inverse design method for circular sliding slopes. A sample set containing 21 successful circular sliding slopes excavated in the past is used to train the network. A test sample of 3 successful circular sliding slopes excavated in the past is used to test the trained network. The test results show that the ANN based inverse design method is valid and can be applied to the design of circular sliding slopes.
In order to improve generalization capability of neural networks, a model structure of Case-Based neural networks has been presented. The model blended Case-Based Reasoning method into neural networks and has the ability of incrementally learning. The results demonstrated that the model could observably improve the generalization capability of supervised neural networks. Firstly, paper summarized the advancing front of researching on generalization capability of neural networks.Secondly, the structure of CBNN and its process of working were introduced. Finally, the results of experiments were compared and discussed.
Nadia Bhuiyan; Adil Salam; Fantahun M. Defersha
Bombardier Aerospace’s high performance aircrafts and services set the utmost standard for the Aerospace industry. A case study in collaboration with Bombardier Aerospace is conducted in order to estimate the target cost of a landing gear. More precisely, the study uses both parametric model and neural network models to estimate the cost of main landing gears, a major aircraft commodity. A comparative analysis between the parametric based model and those upon neural networks model will be con...
Zoeller, Andreas [Physik Department E15, Technische Universitaet Muenchen, 85748 Garching (Germany); Collaboration: CRESST-Collaboration
We present a method based on an Artificial Neural Network for a pulse shape analysis in cryogenic detectors. To train the neural network a huge amount of pulses with known properties are necessary. Therefore, a data-driven simulation used to generate these sets is explained. Furthermore, these simulations allow detailed studies, especially of the cut efficiency and the signal purity of the developed cut. First results are presented and compared with the performance of alternative algorithms.
Eva Volna; Martin Kotyrba; Hashim Habiballa
The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experim...
We present a method based on an Artificial Neural Network for a pulse shape analysis in cryogenic detectors. To train the neural network a huge amount of pulses with known properties are necessary. Therefore, a data-driven simulation used to generate these sets is explained. Furthermore, these simulations allow detailed studies, especially of the cut efficiency and the signal purity of the developed cut. First results are presented and compared with the performance of alternative algorithms.
Shi Qiang Liu; Rong Zhu
Errors compensation of micromachined-inertial-measurement-units (MIMU) is essential in practical applications. This paper presents a new compensation method using a neural-network-based identification for MIMU, which capably solves the universal problems of cross-coupling, misalignment, eccentricity, and other deterministic errors existing in a three-dimensional integrated system. Using a neural network to model a complex multivariate and nonlinear coupling system, the errors could be readily...
Bao, Hai-Bo; Cao, Jin-De
This paper investigates the projective synchronization of fractional-order memristor-based neural networks. Sufficient conditions are derived in the sense of Caputo's fractional derivation and by combining a fractional-order differential inequality. Two numerical examples are given to show the effectiveness of the main results. The results in this paper extend and improve some previous works on the synchronization of fractional-order neural networks. PMID:25463390
Uzunovic, Tarik; Golubovic, Edin; Baran, Eray Abdurrahman; Şabanoviç, Asif; SABANOVIC, Asif
This paper describes configuration space control of a Delta robot with a neural network based kinematics. Mathematical model of the kinematics for parallel Delta robot used for manipulation purposes in microfactory was validated, and experiments showed that this model is not describing “real” kinematics properly. Therefore a new solution for kinematics mapping had to be investigated. Solution was found in neural network utilization, and it was used to model robot's inverse kinematics. It show...
J. C. Ochoa-Rivera; R. García-Bartual; Andreu, J.
A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN) deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generatio...
J. C. Ochoa-Rivera; R. García-Bartual; Andreu, J.
A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN) deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for s...
Sivatte Adroer, Mauricio; Llanas Parra, Francesc Xavier; Buj Corral, Irene; Vivancos Calvet, Joan
In the present paper an indirect model based on neural networks is presented for modelling the rough honing process. It allows obtaining values to be set for different process variables (linear speed, tangential speed, pressure of abrasive stones, grain size of abrasive and density of abrasive) as a function of required average roughness Ra. A multilayer perceptron (feedforward) with a backpropagation (BP) training system was used for defining neural networks. Several configurations were test...
Full Text Available A new approach is presented for identifying material model parameters. The approach is based on coupling stochastic nonlinear analysis and an artificial neural network. The model parameters play the role of random variables. The Monte Carlo type simulation method is used for training the neural network. The feasibility of the presented approach is demonstrated using examples of high performance concrete for prestressed railway sleepers and an example of a shear wall failure.
Along Yu; Zheng Li
In this paper, we present a method used to the numeral eddy current sensor modeling based on genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method,the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line scaling and high precision. The maximum nonlinearity error can be reduced to 0.037% using GNN. However, the maximum nonlinearity error is 0.075% using least square method (LMS).
Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.
The title of the book, "Neural Networks and Micromechanics," seems artificial. However, the scientific and technological developments in recent decades demonstrate a very close connection between the two different areas of neural networks and micromechanics. The purpose of this book is to demonstrate this connection. Some artificial intelligence (AI) methods, including neural networks, could be used to improve automation system performance in manufacturing processes. However, the implementation of these AI methods within industry is rather slow because of the high cost of conducting experiments using conventional manufacturing and AI systems. To lower the cost, we have developed special micromechanical equipment that is similar to conventional mechanical equipment but of much smaller size and therefore of lower cost. This equipment could be used to evaluate different AI methods in an easy and inexpensive way. The proved methods could be transferred to industry through appropriate scaling. In this book, we describe the prototypes of low cost microequipment for manufacturing processes and the implementation of some AI methods to increase precision, such as computer vision systems based on neural networks for microdevice assembly and genetic algorithms for microequipment characterization and the increase of microequipment precision.
This paper reports that a class of artificial intelligence (AI) programs known as neural-networks are well suited to pattern recognition. A neural network is trained rather than programmed to recognize patterns. This differs from expert system AI programs in that it is not following an extensive set of rules determined by the programmer, but rather bases its decision on a gestalt interpretation of the image. The bullseye images from cardiac stress thallium tests performed on 50 male patients, as well as several simulated images were used to train the network. The network was able to accurately classify all patients in the training set. The network was then tested against 50 unknown patients and was able to correctly categorize 77% of the areas of ischemia and 92% of the areas of infarction. While not yet matching the ability of the trained physician, the neural network shows great promise in this area and has potential application in other areas of medical imaging
Wang, David C.; Karvelis, K. C.
A class of artificial intelligence (Al) programs known as neural networks are well suited to pattern recognition. A neural network is trained rather than programmed to recognize patterns. This differs from "expert system" Al programs in that it is not following an extensive set of rules determined by the programmer, but rather bases its decision on a gestalt interpretation of the image. The "bullseye" images from cardiac stress thallium tests performed on 50 male patients, as well as several simulated images were used to train the network. The network was able to accurately classify all patients in the training set. The network was then tested against 50 unknown patients and was able to correctly categorize 77% of the areas of ischemia and 92% of the areas of infarction. While not yet matching the ability of a trained physician, the neural network shows great promise in this area and has potential application in other areas of medical imaging.
Snyder, Robin M.
Motivated by the brain, neural networks are a right-brained approach to artificial intelligence that is used to recognize patterns based on previous training. In practice, one would not program an expert system to recognize a pattern and one would not train a neural network to make decisions from rules; but one could combine the best features of…